Multilevel Reliability Measures of Latent Scores Within an Item Response Theory Framework

Abstract

This paper evaluated multilevel reliability measures in two-level nested designs (e.g., students nested within teachers) within an item response theory framework. A simulation study was implemented to investigate the behavior of the multilevel reliability measures and the uncertainty associated with the measures in various multilevel designs regarding the number of clusters, cluster sizes, and intraclass correlations (ICCs), and in different test lengths, for two parameterizations of multilevel item response models with separate item discriminations or the same item discrimination over levels. Marginal maximum likelihood estimation (MMLE)-multiple imputation and Bayesian analysis were employed to evaluate the accuracy of the multilevel reliability measures and the empirical coverage rates of Monte Carlo (MC) confidence or credible intervals. Considering the accuracy of the multilevel reliability measures and the empirical coverage rate of the intervals, the results lead us to generally recommend MMLE-multiple imputation. In the model with separate item discriminations over levels, marginally acceptable accuracy of the multilevel reliability measures and empirical coverage rate of the MC confidence intervals were found in a limited condition, 200 clusters, 30 cluster size, .2 ICC, and 40 items, in MMLE-multiple imputation. In the model with the same item discrimination over levels, the accuracy of the multilevel reliability measures and the empirical coverage rate of the MC confidence intervals were acceptable in all multilevel designs we considered with 40 items under MMLE-multiple imputation. We discuss these findings and provide guidelines for reporting multilevel reliability measures.     

Links between self-reported and laboratory behavioral impulsivity

Havik, M., Jakobson, A., Tamm, M., Paaver, M., Konstabel, K., Uusberg, A., Allik, J., Ööpik, V. & Kreegipuu, K. (2012). Links between self‐reported and laboratory behavioral impulsivity. Scandinavian Journal of Psychology 53, 216–223. A major problem in the research considering impulsivity is the lack of mutual understanding on how to measure and define impulsivity. Our study examined the relationship between self‐reported impulsivity, behavioral excitatory and inhibitory processes and time perception. Impulsivity – fast, premature, thoughtless or disinhibited behavior – was assessed in 58 normal, healthy participants (30 men, mean age 21.9 years). Self‐reported impulsivity as measured by Adaptive and Maladaptive Impulsivity Scale (AMIS) and behavioral excitatory and inhibitory processes as measured by Stop Signal Task were not directly related. Time perception, measured by the retrospective Time Estimation Task, was related to both. The length of the perceived time interval was positively correlated to AMIS Disinhibition subscale and negatively to several Stop Signal Task parameters. The longer subjects perceived the duration to last, the higher was their score on Disinhibition scale and the faster were their reactive responses in the Stop Signal Task. In summary our findings support the idea of cognitive tempo as a possible mechanism underlying impulsive behavior.     

The effect of food toppings on calorie estimation and consumption

In this research we examine the effect of adding a food topping to the base food on consumers' calorie estimation and consumption of the augmented food (base food plus topping). We show that consumers underestimate the calorie content of augmented food with an unhealthy base, especially when the topping is healthy. However, consumers are less likely to underestimate the calorie content of augmented food with a healthy base, regardless of whether the topping is healthy or unhealthy. Further, we show that adding a healthy topping to an unhealthy base makes consumers not only underestimate the calorie content but also eat more of the augmented food.     

潜变量交互效应标准化估计：方法比较与选用策略

标准化估计对模型的解释和效应大小的比较有重要作用。虽然潜变量交互效应的恰当标准化估计公式已经面世超过10年, 国内外都在使用和引用, 但至今未见到关于不同估计方法得到的恰当标准化估计的系统比较。通过模拟实验, 比较了乘积指标法、潜调节结构方程(LMS)、无先验信息和有先验信息的贝叶斯法的潜变量交互效应标准化估计在不同条件下的表现。结果发现, 在正态条件下, LMS和有信息贝叶斯法表现较好; 而在非正态条件下, 乘积指标法比较稳健, 但需要较大的样本(不小于500), 小样本且外生潜变量之间相关很低时可使用无信息贝叶斯法。     

认知诊断缺失数据处理方法的比较：零替换、多重插补与极大似然估计法

数据缺失在测验中经常发生, 认知诊断评估也不例外, 数据缺失会导致诊断结果的偏差。首先, 通过模拟研究在多种实验条件下比较了常用的缺失数据处理方法。结果表明：(1)缺失数据导致估计精确性下降, 随着人数与题目数量减少、缺失率增大、题目质量降低, 所有方法的PCCR均下降, Bias绝对值和RMSE均上升。(2)估计题目参数时, EM法表现最好, 其次是MI, FIML和ZR法表现不稳定。(3)估计被试知识状态时, EM和FIML表现最好, MI和ZR表现不稳定。其次, 在PISA2015实证数据中进一步探索了不同方法的表现。综合模拟和实证研究结果, 推荐选用EM或FIML法进行缺失数据处理。     

建构反应题中能力估计准确性的影响因素:评分者人数和项目个数的交互作用

采用康春花、孙小坚和曾平飞(2016)提出的等级反应多水平侧面模型探讨了评分者人数和项目个数对被试能力估计准确性的影响。模拟研究的结果表明:(1)随着项目个数的增加,估计值与真值之间的相关也不断增加;(2)评分者人数和项目个数在平均绝对偏差(MAB)和误差均方根(RMSE)上的主效应均显著,两者间的交互效应也显著;(3)简单效应分析发现,当项目较少时,3个评分者条件下的能力估计准确性最好; 随着项目个数的增加,4个评分者的估计误差迅速下降,且表现变为最好。     

Two-Stage maximum likelihood estimation in the misspecified restricted latent class model

The maximum likelihood classification rule is a standard method to classify examinee attribute profiles in cognitive diagnosis models (CDMs). Its asymptotic behaviour is well understood when the model is assumed to be correct, but has not been explored in the case of misspecified latent class models. This paper investigates the asymptotic behaviour of a two-stage maximum likelihood classifier under a misspecified CDM. The analysis is conducted in a general restricted latent class model framework addressing all types of CDMs. Sufficient conditions are proposed under which a consistent classification can be obtained by using a misspecified model. Discussions are also provided on the inconsistency of classification under certain model misspecification scenarios. Simulation studies and a real data application are conducted to illustrate these results. Our findings can provide some guidelines as to when a misspecified simple model or a general model can be used to provide a good classification result.     

Subjective,physiological, and behavioural responses towards evaluatively conditioned stimuli

Evaluative Conditioning (EC) is commonly defined as the change in liking of a stimulus (conditioned stimulus, CS) due to its pairings with an affective unconditioned stimulus (US). In Experiment 1, we investigated effects of repeated stimulus pairings on affective responses, i.e. valence and arousal ratings, pupil size, and duration estimation. After repeatedly pairing the CSs with affective USs, a consistent pattern of affective responses emerged: The CS_negative was rated as being more negative and more arousing, resulted in larger pupils, and was temporally overestimated compared to the CS_neutral. In Experiment 2, the influence of a mere instruction about the contingency between a CS and US on affective responses was examined. After mere instruction about upcoming pairings between the CS and US, subjective ratings also changed, but there was neither evidence for differential pupillary responses nor for differential temporal processing. The results indicate that EC via pairings or instructions can change the affective responses towards formerly neutral stimuli and introduce pupil size as a physiological measure in EC research. However, Experiment 2 suggests that there might be moderating factors based on the type of EC procedure involved.     

Integrating Covariates into Social Relations Models: A Plausible Values Approach for Handling Measurement Error in Perceiver and Target Effects

The Social Relations Model (SRM) is a conceptual and analytical approach to examining dyadic behaviors and interpersonal perceptions within groups. In an SRM, the perceiver effect describes a person's tendency to perceive other group members in a certain way, whereas the target effect measures the tendency to be perceived by others in certain ways. In SRM research, it is often of interest to relate these individual SRM effects to covariates. However, the estimated individual SRM effects might not provide a very reliable measure of the true, unobserved SRM effects, resulting in distorted estimates of associations with other variables. This article introduces a plausible values approach that allows users to correct for measurement error when assessing the association of individual SRM effects with other individual difference variables. In the plausible values approach, the latent, true individual SRM effects are treated as missing values and are imputed from an imputation model by applying Bayesian estimation techniques. In a simulation study, the statistical properties of the plausible values approach are compared with two approaches that have been used in previous research. A data example from educational psychology is presented to illustrate how the plausible values approach can be implemented with the software WinBUGS.