The factor structure of the Social Axioms Survey II (SASII) in the South African context

The objective of this study was to validate the new Social Axioms Survey II (SASII). The study sample comprised university students (n = 793) as well as their family members and friends (n = 645). Confirmatory factor analysis (CFA) following an initial exploratory factor analysis yielded a five-factor model: social cynicism, social complexity, reward for application, religiosity, and fate control. The findings of this study support the use of the new SASII in South Africa for research purposes.     

Investigating Separate and Concurrent Approaches for Item Parameter Drift in 3PL Item Response Theory Equating

This study examines separate and concurrent approaches to combine the detection of item parameter drift (IPD) and the estimation of scale transformation coefficients in the context of the common item nonequivalent groups design with the three-parameter item response theory equating. The study uses real and synthetic data sets to compare the two approaches based on IPD flagging rates, type I error and power rates, and recovery of scale transformation coefficients. Results indicate that the two approaches render similar outcomes with stable anchor sets. However, they can produce dissimilar results with unstable anchor sets because of differences in the performance of their IPD components. Further, the findings of this study caution about working backward from equated cut scores to motivate the selection of an anchor set.     

改进的MCMC算法及其在估计IRT模型参数中的应用

本文首先简要的阐述了MCMC算法的思想及在IRT参数估计中的操作过程;其次,针对该算法存在的一些问题,提出相应的改进建议;然后,分别运用传统的和改进型的MCMC算法进行模拟数据分析和比较,结果显示新的方法表现更好;最后总结新方法的优点所在,并指出下一步的研究方向。     

自定步调学习时间的习惯性反应：来自眼动的证据

习惯性反应指学习者根据自身阅读习惯来进行学习时间分配,它通常由词对位置这一外部线索激发。研究运用眼动记录技术,采用Metcalfe范式探讨词对位置和难度对学习时间分配的影响,以检验自定步调学习时间的习惯性反应。结果发现：（1）在自定步调总学习时间上,学习者倾向于对难度越大的项目分配越多的学习时间;（2）在前期自定步调学习进程上,当词对位置为易-中-难条件时,学习者倾向于优先在容易项目上分配较多的学习时间,接着是中等难度项目,最后是困难项目;当词对位置为难-中-易条件时,结果相反。这说明学习者的自定步调总学习时间受项目难度驱动,而前期自定步调学习时间受习惯性反应影响。     

How to get really smart: Modeling retest and training effects in ability testing using computer-generated figural matrix items

The interpretation of retest scores is problematic because they are potentially affected by measurement and predictive bias, which impact construct validity, and because their size differs as a function of various factors. This paper investigates the construct stability of scores on a figural matrices test and models retest effects at the level of the individual test taker as a function of covariates (simple retest vs. training, use of identical vs. parallel retest forms, and general mental ability). A total of N = 189 subjects took two tests of matrix items that were automatically generated according to a strict construction rationale. Between test administrations, participants in the intervention groups received training, while controls did not. The Rasch model fit the data at both time points, but there was a lack of item difficulty parameter invariance across time. Training increased test performance beyond simple retesting, but there was no large difference between the identical and parallel retest forms at the individual level. Individuals varied greatly in how they profited from retest experience, training, and the use of identical vs. parallel retest forms. The results suggest that even with carefully designed tasks, it is problematic to directly compare scores from initial tests and retests. Test administrators should emphasize learning potential instead of state level assessment, and inter-individual differences with regard to test experience should be taken into account when interpreting test results.     

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。