Bayesian Estimation for Item Factor Analysis Models with Sparse Categorical Indicators

Psychometric models for item-level data are broadly useful in psychology. A recurring issue for estimating item factor analysis (IFA) models is low-item endorsement (item sparseness), due to limited sample sizes or extreme items such as rare symptoms or behaviors. In this paper, I demonstrate that under conditions characterized by sparseness, currently available estimation methods, including maximum likelihood (ML), are likely to fail to converge or lead to extreme estimates and low empirical power. Bayesian estimation incorporating prior information is a promising alternative to ML estimation for IFA models with item sparseness. In this article, I use a simulation study to demonstrate that Bayesian estimation incorporating general prior information improves parameter estimate stability, overall variability in estimates, and power for IFA models with sparse, categorical indicators. Importantly, the priors proposed here can be generally applied to many research contexts in psychology, and they do not impact results compared to ML when indicators are not sparse. I then apply this method to examine the relationship between suicide ideation and insomnia in a sample of first-year college students. This provides an important alternative for researchers who may need to model items with sparse endorsement.     

A tutorial on how to do a Mokken scale analysis on your test and questionnaire data

Over the past decade, Mokken scale analysis (MSA) has rapidly grown in popularity among researchers from many different research areas. This tutorial provides researchers with a set of techniques and a procedure for their application, such that the construction of scales that have superior measurement properties is further optimized, taking full advantage of the properties of MSA. First, we define the conceptual context of MSA, discuss the two item response theory (IRT) models that constitute the basis of MSA, and discuss how these models differ from other IRT models. Second, we discuss dos and don'ts for MSA; the don'ts include misunderstandings we have frequently encountered with researchers in our three decades of experience with real‐data MSA. Third, we discuss a methodology for MSA on real data that consist of a sample of persons who have provided scores on a set of items that, depending on the composition of the item set, constitute the basis for one or more scales, and we use the methodology to analyse an example real‐data set.     

Bayesian analysis of longitudinal multitrait–multimethod data with ordinal response variables

A new multilevel latent state graded response model for longitudinal multitrait–multimethod (MTMM) measurement designs combining structurally different and interchangeable methods is proposed. The model allows researchers to examine construct validity over time and to study the change and stability of constructs and method effects based on ordinal response variables. We show how Bayesian estimation techniques can address a number of important issues that typically arise in longitudinal multilevel MTMM studies and facilitates the estimation of the model presented. Estimation accuracy and the impact of between‐ and within‐level sample sizes as well as different prior specifications on parameter recovery were investigated in a Monte Carlo simulation study. Findings indicate that the parameters of the model presented can be accurately estimated with Bayesian estimation methods in the case of low convergent validity with as few as 250 clusters and more than two observations within each cluster. The model was applied to well‐being data from a longitudinal MTMM study, assessing the change and stability of life satisfaction and subjective happiness in young adults after high‐school graduation. Guidelines for empirical applications are provided and advantages and limitations of a Bayesian approach to estimating longitudinal multilevel MTMM models are discussed.     

允许CAT题目检查的区块题目袋方法

允许题目检查能够促进计算机化自适应测验(CAT)在实际中的应用。在不影响能力估计精度和测验公平性的前提下, 允许CAT题目检查能够缓解考生考试焦虑, 减少无关因素引起的测量误差。区块题目袋方法是连续区块方法与题目袋方法的结合, 不仅能允许CAT题目检查, 还能够弥补题目袋方法的不足。研究结果表明：(1)合理作答策略下, 区块题目袋方法的估计精度在低能力水平上要优于题目袋方法; (2)在应对类似Wainer作答策略时, 区块题目袋方法的估计精度在所有能力水平上均优于题目袋方法。(3)随着区块数的增加, 区块题目袋方法的能力估计精度越接近无修改的基线水平。     

Modeling storage and retrieval processes with clinical populations with applications examining alcohol-induced amnesia and Korsakoff amnesia

Multinomial processing tree models can provide for measures of underlying cognitive processes. In this paper, the Chechile [Chechile, R. A. (2004). New multinomial models for the Chechile-Meyer task. Journal of Mathematical Psychology, 48, 364-384] 6P model is described and applied to several applications involving clinical populations. The model provides for separate measures of storage and retrieval. Monte Carlo studies were conducted to examine the relative accuracy of two methods for obtaining an overall condition estimate for the 6P model, i.e., averaging estimates found for individuals versus pooling the multinomial frequency data before estimating the model parameters. The sampling studies showed that the pooling of frequencies resulted in more accurate parameter estimates. However, psychological assessment in clinical psychology requires precise measurement on an individual basis. In order to recover information about individuals from pooled frequency information, a modified jackknife method was advanced. The jackknife method is based on a contrast between the overall pooled frequency information and the pool frequency without the observations from a single individual. Another series of Monte Carlo simulations demonstrate that the new jackknife method resulted in better recovery of the correct individual parameter values relative to estimates based on only the data from the individual. Finally, the 6P model was used to examine the data from two previously reported studies with clinical populations. One application addressed the effect of alcohol-induced amnesia, and the other application dealt with Korsakoff amnesia. In both cases the pattern of storage and retrieval measurements resulted in a clarification of the underlying storage and retrieval differences between the clinical group and the control group.     

Avoiding the effect of item wording by means of bipolar instead of unipolar items: An application to social optimism

Scales including positively and negatively worded items usually show an impaired degree of homogeneity. The transformation of unipolar positively and negatively worded items into bipolar items can avoid this disadvantageous effect. The precondition for this transformation is that each pair of items refers to the same topic. It is this topic that serves as the heading of the bipolar item. This scale construction method is demonstrated in the items of the social optimism scale (Schweizer & Schneider, 1997) that comprises unipolar items. The investigation of both the original and the transformed scales in a sample of 808 participants revealed equivalence and a high quality for both scales. Results of an additional sample confirmed the validity of both social optimism scales. Copyright © 2009 John Wiley & Sons, Ltd.     

测验项目反应机制与心理测量模型假设的对应性分析

从测验项目解决的认知过程的视角分析了在不同测验理论框架下的测量模型中的基本假设, 指出测量模型是测验开发者有关测验项目反应机制的理论假设的具体表征, 是系统检验测量假设和过程的统计框架。然而, 不管是经典测验理论、概化理论, 还是早期的项目反应理论模型, 相关假设都过于简化, 缺少相应实质理论的支持。与之相比, 认知测量模型强调与个体在测验项目反应过程中的认知过程、认知策略和知识结构的对应性, 提供了在实质理论基础上界定测量建构、设计测验项目、进行建模分析和解释的可能性, 为日益边缘化的心理测量学和主流心理学研究的融合奠定了基础。     

内侧颞叶与来源记忆

来源记忆不同于项目记忆。早期的研究认为来源记忆的神经基础主要是前额叶,但近几年的相关研究发现内侧颞叶在来源记忆中的作用也是相当重要的,健康被试的功能神经成像研究和内侧颞叶损伤病人研究都为内侧颞叶在来源记忆中的作用提供了可靠证据。     

语言与儿童心理理论关系的一种新观点

语言与心理理论的关系是儿童研究领域的研究者们长期探讨的话题,大量的实验结果表明二者的确存在密切的关系,但具体机制尚未弄清。该文介绍了探讨二者关系的一种新思路,即把心理理论重铸为对认知灵活性的测量,来考察语言在心理理论中的作用。文中介绍了语言的标签效应对认知灵活性影响的实验证据,并预期了在心理理论任务中使用语言标签可能出现的结果。