Increasing item complexity: A possible cause of scale shrinkage for unidimensional item response theory

When the three-parameter logistic model is applied to tests covering a broad range of difficulty, there frequently is an increase in mean item discrimination and a decrease in variance of item difficulties and traits as the tests become more difficult. To examine the hypothesis that this unexpected scale shrinkage effect occurs because the items increase in complexity as they increase in difficulty, an approximate relationship is derived between the unidimensional model used in data analysis and a multidimensional model hypothesized to be generating the item responses. Scale shrinkage is successfully predicted for several sets of simulated data.The author is grateful to Robert Mislevy for kindly providing a copy of his computer program, RESOLVE.     

华人人际关系的概念化——针对中国香港大学生的实证研究

以杨中芳所构建的华人人际关系概念化理论模型为蓝本,并用以往的实证研究方法为基础,揭示了当代华人对人际关系如何进行概念化及其潜在的认知维度。杨氏模型认为华人人际关系主要由既定成分、情感成分和工具成分组成。该研究以中国香港大学生为被试进行调查,首先采用访谈的形式,分家族、学校及日常生活情境建立人际关系清单。然后采用多维量法（Multi-dimensional scaling）处理被试对关系进行分类的数据。结果发现,在不同的生活情境中,被试均依据情感程度对关系进行分类。在等级差异明显的家族和学校情境中,被试依据辈份和等级差异的突显性进行区分;而在等级差异小的生活情境中,则采用功利程度为标准。本文对被试在不同情境中所采用的不同维度进行了讨论,并提出华人人际关系的义务具有工具性质的实证结果     

高中英语学习策略量表编制与Rasch多维度分析

当前,语言学习策略已成为我国中学英语教学的一项重要目标。该研究通过科学设计的问卷,调查了406名高三学生的英语学习策略掌握情况,并进一步分三个分维度、十个子维度,进行了深入的分析。研究表明,问卷提供的多维度的详细信息可以帮助详细了解学生对策略的掌握情况,从而进行有针对性的指导。同时,问卷的设计,维度划分等,也为研究人员,高中英语学习策略的教材编写和教学提供可靠有用的参考。     

Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.     

Integrating Cognitive Process and Descriptive Models of Attitudes and Preferences

Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well‐established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi‐attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator‐based models of decision making used in response time research can also provide process‐level instantiations for random utility models.     

A multidimensional approach to measuring well-being in students: Application of the PERMA framework

Seligman recently introduced the PERMA model with five core elements of psychological well-being: positive emotions, engagement, relationships, meaning, and accomplishment. We empirically tested this multidimensional theory with 516 Australian male students (age 13–18). From an extensive well-being assessment, we selected a subset of items theoretically relevant to PERMA. Factor analyses recovered four of the five PERMA elements, and two ill-being factors (depression and anxiety). We then explored the nomological net surrounding each factor by examining cross-sectional associations with life satisfaction, hope, gratitude, school engagement, growth mindset, spirituality, physical vitality, physical activity, somatic symptoms, and stressful life events. Factors differentially related to these correlates, offering support for the multidimensional approach to measuring well-being. Directly assessing subjective well-being across multiple domains offers the potential for schools to more systematically understand and promote well-being.     

The moderating effects of gender on the associations between multidimensional hostility and psychosomatic symptoms: a Chinese case

The purpose of this study was to examine the effects of gender on the relationship between multidimensional hostility and psychosomatic symptoms in Chinese culture. The participants in this study were 398 Chinese college students (40% female) recruited from Taiwan. Four dimensions of multidimensional hostility-hostility cognition, hostility affect, expressive hostility behavior, and suppressive hostility behavior-were measured by the Chinese Hostility Inventory. After controlling for the effects of depression and anxiety, the results of path analysis revealed that the multidimensional hostility predicted psychosomatic symptoms directly, and predicted psychosomatic symptoms indirectly through negative health behavior. Furthermore, gender moderated the relationships between multidimensional hostility and health outcomes. Expressive hostility exacerbated psychosomatic symptom in females but buffered it in males, while affective hostility exacerbated psychosomatic symptoms in males. Additionally, suppressive hostility behavior was correlated to psychosomatic symptoms indirectly through negative health behavior in females. Moreover, expressive hostility was correlated to psychosomatic symptoms indirectly through negative health behavior more in males than in females.     

Multilevel multidimensional item response model with a multilevel latent covariate

In a pre‐test–post‐test cluster randomized trial, one of the methods commonly used to detect an intervention effect involves controlling pre‐test scores and other related covariates while estimating an intervention effect at post‐test. In many applications in education, the total post‐test and pre‐test scores, ignoring measurement error, are used as response variable and covariate, respectively, to estimate the intervention effect. However, these test scores are frequently subject to measurement error, and statistical inferences based on the model ignoring measurement error can yield a biased estimate of the intervention effect. When multiple domains exist in test data, it is sometimes more informative to detect the intervention effect for each domain than for the entire test. This paper presents applications of the multilevel multidimensional item response model with measurement error adjustments in a response variable and a covariate to estimate the intervention effect for each domain.     

Ethical Academic Judgments and Behaviors: Applying a Multidimensional Ethics Scale to Measure the Ethical Academic Behavior of Graduate Students

Using Reidenbach and Robin's Multidimensional Ethics Scale, this study investigated the relationships between background variables and students' ethical evaluations, judgments, and behavioral intentions using 3 scenarios involving dilemmas related to academic dishonesty. The sample included 436 master's students and 142 doctoral students. The study found that the participants used a combination of ethical philosophies to make ethical decisions. The respondents judged improper citations more harshly than acts of inappropriate authorship or the falsification of data. The doctoral students generally considered behaviors related to plagiarism and falsification to be more unethical than the master's students did, though no gender differences were found.     

A Multidimensional Scaling Analysis of Perceptions of Academic Dishonesty Among Fifth-Grade Students

A study was conducted to investigate the perceptions of academic dishonesty in fifth-grade students. Two methods were used to gather data: a sorting task, which was used to indirectly assess the students' perceptions, and a rating scale task, which was used to externally validate the results of the sorting task. Results of the multidimensional scaling analysis yielded two dimensions, the first being tests/homework and papers, and the second, more ambiguous appearing to differentiate based on seriousness.