A comment on ‘some standard errors in item response theory’

In maximum likelihood estimation the standard error of the location parameter of the three parameter logistic model can be large, due to inaccurate estimation of the lower asymptote. Thissen and Wainer who demonstrated this effect, suggested that the introduction of a prior distribution for the lower asymptote might alleviate the problems. Here it is demonstrated in some detail that the standard error of the location parameter can be made acceptably small in this way.The author thanks Pieter Vijn for his helpful comments.     

Optimal item difficulty for the three-parameter normal ogive response model

In tailored testing, it is important to determine the optimal difficulty of the next item to present to the examinee. This paper shows that the difference that maximizes information for the three-parameter normal ogive response model is approximately 1.7 times the optimal difference –b for the three-parameter logistic model. Under the normal model, calculation of the optimal difficulty for minimizing the Bayes risk is equivalent to maximizing an associated information function.The views expressed herein, are those of the author and do not necessarily reflect those of the Department of the Navy.     

The bias function of the maximum likelihood estimate of ability for the dichotomous response level

Samejima has recently given an approximation for the bias function for the maximum likelihood estimate of the latent trait in the general case where item responses are discrete, generalizing Lord's bias function in the three-parameter logistic model for the dichotomous response level. In the present paper, observations are made about the behavior of this bias function for the dichotomous response level in general, and also with respect to several widely used mathematical models. Some empirical examples are given.     

Measurement invariance,factor analysis and factorial invariance

Several concepts are introduced and defined: measurement invariance, structural bias, weak measurement invariance, strong factorial invariance, and strict factorial invariance. It is shown that factorial invariance has implications for (weak) measurement invariance. Definitions of fairness in employment/admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness/equity to exist. Implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers.Presidential address delivered at the Annual Meeting of the Psychometric Society in Berkeley, California, June 18–20, 1993.     

An approximation for the bias function of the maximum likelihood estimate of a latent variable for the general case where the item responses are discrete

Lord developed an approximation for the bias function for the maximum likelihood estimate in the context of the three-parameter logistic model. Using Taylor's expansion of the likelihood equation, he obtained an equation that includes the conditional expectation, given true ability, of the discrepancy between the maximum likelihood estimate and true ability. All terms of orders higher thann ^?1 are ignored wheren indicates the number of items. Lord assumed that all item and individual parameters are bounded, all item parameters are known or well-estimated, and the number of items is reasonably large. In the present paper, an approximation for the bias function of the maximum likelihood estimate of the latent trait, or ability, will be developed using the same assumptions for the more general case where item responses are discrete. This will include the dichotomous response level, for which the three-parameter logistic model has been discussed, the graded response level and the nominal response level. Some observations will be made for both dichotomous and graded response levels.     

信度系数与观测分数和潜在特质的相关比

在经典真分数模型中,信度系数R=D(T)/D(X)。通常认为,信度系数与项目反应理论没有什么联系。事实上,信度系数恰好等于考生观测分数与潜在特质分数的非线性相关比η_(xθ)~2=1-MD(x|θ)/D(X),据此我们得出估计信度系数的一种新途径,同时讨论了x、θ的相关系数与相关比η_(xθ)~2的关系。     

An exact and optimal standardized person test for assessing consistency with the rasch model

The Rasch model predicts that an individual's ability level is invariant over subtests of the total test, and thus, all subtests measure the same latent trait. A person test of this invariance hypothesis is discussed that is uniformly most powerful and standardized in the sense that the conditional distribution of the test statistic, given a particular level of ability, does not depend on the absolute value of the examinee's ability parameter. The test can be routinely performed by applying a computer program designed by and obtainable from the author. Finally, a suboptimal test is derived that is extremely easy to use, and an overall group test of the invariance hypothesis discussed. All tests considered do not rely on asymptotic approximations; hence, they may be applied when the test is of only moderate length and the group of examinees is small.     

An extension of the rating scale model with an application to the measurement of change

The polytomous unidimensional Rasch model with equidistant scoring, also known as the rating scale model, is extended in such a way that the item parameters are linearly decomposed into certain basic parameters. The extended model is denoted as the linear rating scale model (LRSM). A conditional maximum likelihood estimation procedure and a likelihood-ratio test of hypotheses within the framework of the LRSM are presented. Since the LRSM is a generalization of both the dichotomous Rasch model and the rating scale model, the present algorithm is suited for conditional maximum likelihood estimation in these submodels as well. The practicality of the conditional method is demonstrated by means of a dichotomous Rasch example with 100 items, of a rating scale example with 30 items and 5 categories, and in the light of an empirical application to the measurement of treatment effects in a clinical study.Work supported in part by the Fonds zur Förderung der Wissenschaftlichen Forschung under Grant No. P6414.     

情绪调节灵活性对负性情绪的影响：来自经验取样的证据

情绪调节灵活性是指个体根据不断变化的情境需求灵活部署情绪调节策略的能力。本研究采用经验取样方法,通过拟合个体在日常生活事件(如,未通过考试)和突发公共卫生事件(COVID-19)中的策略使用剖面结构和情境负性程度与策略使用程度的共变关系测量个体的情绪调节灵活性水平,并探讨其对个体后续负性情绪(抑郁和焦虑)的影响。两个独立样本结果表明：单一策略使用偏好(如沉浸偏好和表达抑制偏好)的个体在负性生活事件中和疫情期间经历了更高水平的抑郁和焦虑情绪。此外,当个体随情境负性程度提高使用更多分心策略,而随情境负性程度降低使用更多认知重评策略(意味较高的情绪调节灵活性),其抑郁和焦虑情绪水平更低。以上结果共同证实了情绪调节灵活性有利于减少个体的负性情绪体验。     

Selecting entrepreneurial employees using a person-group fit perspective

As jobs become unstructured and collective endeavor oriented, it is increasingly being realized that work groups must become more autonomous and entrepreneurial. The selection literature is however silent on the predictive mechanisms that may be leveraged to select group members with the desired competencies. The person-group fit perspective enables us to hypothesize and demonstrate that selection instruments geared toward gauging an individual's fit within a group are likely to manifest entrepreneurial competencies and behaviors in the individual. Further, the nature of the job, in terms of the structuredness of work (task formalization) and the repetitiveness of work activities (task routinization), has a moderating impact on the relationship between overall entrepreneurial competence and selection practices. The study has implications for incorporating the principles of person-group fit into the design of job profiles.