Stability of the Millon Clinical Multiaxial Inventory among psychiatric inpatients

This study investigated the stability of the Millon Clinical Multiaxial Inventory (MCMI) in a sample of psychiatric inpatients over a longer time period and from different perspectives than previous reliability studies. Test-retest reliability was assessed from three perspectives: correlation/regression, equivalence of dimensional structures across testings upon different admissions, and stability of personality style across testings. Some stability from a correlational perspective was found, with higher stability estimates indicated for the basic personality scales in comparison with the clinical symptom scales which is consistent with Millon's theory. The internal structure of the MCMI was essentially identical across testings. Although source of reinforcement and instrumental or coping style were stable across testings beyond chance, the MCMI profiles of a considerable number of subjects were inconsistent across testings. Correspondingly, clinicians should be cautious in using the MCMI to make categorical decisions regarding personality style with inpatients who are tested at admission and who have had repeated hospitalizations.     

More “tricks of the trade”: Reparameterizing LISREL models using negative variances

This paper shows how LISREL may be used to estimate simplex models which impose constraints on the variances of endogenous variables. This technique allows us to estimate both the parameters and the standard errors of the correlated measurement error model proposed by Wiley and Wiley (1974).We would like to thank Jim Wiley for his many helpful comments and suggestions on an earlier draft. We are grateful also to an anonymous reviewer for supplying the EQS program presented in Figure 4.     

Women's social experiences before and during the COVID-19 pandemic: A 3-part measurement burst study among women in midlife with health risks

Women in midlife (ages 40–60) with elevated risk for cardiovascular disease (CVD) often rely on social networks during times of high stress. Precautions against the spread of COVID-19—particularly stay-at-home-orders—could have limited positive social experiences during a stressful time, but also could have reduced unwanted negative social experiences. This report presents findings from an ecological momentary assessment study that used 3 bursts of 5 surveys per day for 5 days, to test for changes in women's social experiences: prior to COVID-19 (2019), during stay-at-home-orders (April-May 2020), and during initial reopening (August-September 2020). Participants were women aged 40–60 with one or more CVD risk factors (e.g., hypertension; N = 35, M_Age = 51, M_BMI = 32.2 kg/m²). Momentary reports showed that the number of positive interactions experienced in daily life did not significantly change from before to during the pandemic; positive interactions were more variable during stay-at-home orders than pre-COVID, but rebounded by initial reopening (to pre-COVID levels). In contrast, the number of negative social interactions and social comparisons decreased from before COVID to stay-at-home orders, and remained lower during initial reopening; these experiences were also less variable during stay-at-home orders and initial reopening than before COVID-19. Thus, in a vulnerable group of women with health risks, there is little evidence that social experiences worsened during the first 6 months of the COVID-19 pandemic; decreases in (potentially) negative social experiences may be a small but beneficial side effect of short-term public health precautions.     

Enhancing measurement validity in diverse populations: Modern approaches to evaluating differential item functioning

When developing and evaluating psychometric measures, a key concern is to ensure that they accurately capture individual differences on the intended construct across the entire population of interest. Inaccurate assessments of individual differences can occur when responses to some items reflect not only the intended construct but also construct-irrelevant characteristics, like a person's race or sex. Unaccounted for, this item bias can lead to apparent differences on the scores that do not reflect true differences, invalidating comparisons between people with different backgrounds. Accordingly, empirically identifying which items manifest bias through the evaluation of differential item functioning (DIF) has been a longstanding focus of much psychometric research. The majority of this work has focused on evaluating DIF across two (or a few) groups. Modern conceptualizations of identity, however, emphasize its multi-determined and intersectional nature, with some aspects better represented as dimensional than categorical. Fortunately, many model-based approaches to modelling DIF now exist that allow for simultaneous evaluation of multiple background variables, including both continuous and categorical variables, and potential interactions among background variables. This paper provides a comparative, integrative review of these new approaches to modelling DIF and clarifies both the opportunities and challenges associated with their application in psychometric research.     

The biasing effects of selection and attrition on estimating the mean

Organizational and validation researchers often work with data that has been subjected to selection on the predictor and attrition on the criterion. These researchers often use the data observed under these conditions to estimate either the predictor or criterion's restricted population means. We show that the restricted means due to direct or indirect selection are a function of the population means plus the selection ratios. Thus, any difference between selected mean groups reflects the population difference plus the selection ratio difference. When there is also attrition on the criterion, the estimation of group differences becomes even more complicated. The effect of selection and attrition induces measurement bias when estimating the restricted population mean of either the predictor or criterion. A sample mean observed under selection and attrition does not estimate either the population mean or the restricted population mean. We propose several procedures under normality that yield unbiased estimates of the mean. The procedures focus on correcting the effects of selection and attrition. Each procedure was evaluated with a Monte Carlo simulation to ascertain its strengths and weaknesses. Given appropriate sample size and conditions, we show that these procedures yield unbiased estimators of the restricted and unrestricted population means for both predictor and criterion. We also show how our findings have implications for replicating selected group differences.     

可修改答案的计算机化自适应测验的方法

与传统的纸笔测验(Paper And Pencil Based Test, P&P)相比计算机化自适应测验(Computerized Adaptive Testing, CAT)根据被试的作答反应自适应地选择题目, 它不仅缩短了测验长度, 还极大地提高了测验的准确性。然而, 目前绝大多数CAT不允许被试修改答案, 研究者主要担心修改答案会降低CAT的有效性。允许修改答案符合被试一贯的测验习惯, 修改之后的分数更能反映被试真实的水平, 从而能够进一步促进CAT在实际中的应用。现有的研究主要从三个方面提出了可修改答案CAT的控制方法：一是测验设计; 二是改进选题策略; 三是建构模型。未来的研究应进一步探讨这些方法之间的比较与结合, 以及对可修改答案认知诊断CAT (Cognitive Diagnostic CAT, CD-CAT)的研究。     

怀旧的触发、研究范式及测量

怀旧指的是个体怀念过去的事物、人物、地方甚至经历或感受。它大多与美好的记忆联系在一起, 并会带来积极的情绪体验, 但同时可能也会引起消极的情绪体验。怀旧对维持个体身心健康有一定的积极效用。本文着重介绍了怀旧的触发因素包括客观刺激的视觉、嗅觉、听觉及主观刺激的消极情绪与低温知觉, 分析了其研究范式有质化研究(内容分析与扎根理论)、量化研究(问卷调查与经验取样法)和实验研究(单因素随机实验设计与多因素实验设计), 并介绍了其相关的测量工具。未来研究要加强主动怀旧与被动怀旧、积极怀旧与消极怀旧的探讨; 整合各种研究范式与强化纵向的追踪研究; 以及要区分特质型怀旧与状态型怀旧。     

认知诊断CAT中具有非统计约束选题方法的比较

项目曝光控制和内容约束关系到测验安全、测验的信度和效度, 是计算机化自适应测验(Computerized Adaptive Testing, CAT)中两类重要的非统计约束条件。本文在认知诊断CAT中针对内容约束和项目曝光控制要求, 运用5种方法选择测验项目。它们分别是：(1) Monte Carlo方法与项目合格方法相结合, 记为MC-IE; (2) Monte Carlo方法与最大优先指标方法相结合, 记为MC-MPI; (3) Monte Carlo方法与限制阈值方法相结合, 记为MC-RT; (4) Monte Carlo方法与限制进度指标方法相结合, 记为MC-RPG以及(5) Monte Carlo方法与最大后验概率方法相结合, 记为MC-PP。然后通过在线性、收敛、发散、无结构和独立五种属性结构下构建题库并运用重参化融融统和模型模拟被试反应比较它们的选题表现。研究发现, (1) 相同选题方法在不同属性结构下项目曝光率的分布类似, 测量精度按线性、收敛、发散、无结构和独立结构的顺序依次降低; (2) 相同属性结构下, 不同方法的测量精度高低依次为MC-PP、MC-IE、MC-RT、MC-MPI和MC-RPG方法; 项目曝光均匀性优劣依次为MC-RPG、MC-MPI、MC-RT、MC-IE和MC-PP方法。统一量纲值表明, MC-RPG方法的综合表现最好, MC-MPI方法的表现次之。     

音乐诱发情绪测量及其影响因素*

使用音乐诱发情绪是有效且特殊的诱发方式。本文对音乐诱发情绪的自我报告测量、心理生理测量和功能神经成像测量进行了系统的梳理,并阐述了一种基于音乐特定模型的情绪测量方法。文章从音乐刺激的选择、听者特征以及情境特征三方面对影响音乐情绪测量的因素进行了总结。并为保证测量的准确性进一步提出确定理论模型一致性、结合多元方法、利用有效刺激材料、认真考评情境和参与者特征等解决思路,并阐明了此领域的研究趋势。     

‘Additive difference’ models without additivity and subtractivity

This paper studies conjoint measurement models tolerating intransitivities that closely resemble Tversky's additive difference model while replacing additivity and subtractivity by mere decomposability requirements. We offer a complete axiomatic characterisation of these models without having recourse to unnecessary structural assumptions on the set of objects. This shows the pure consequences of several cancellation conditions that have often been used in the analysis of more traditional conjoint measurement models. Our models contain as particular cases many aggregation rules that have been proposed in the literature.