Exact person fit indexes for the rasch model for arbitrary alternatives

A new algorithm for obtaining exact person fit indexes for the Rasch model is introduced which realizes most powerful tests for a very general family of alternative hypotheses, including tests concerning DIF as well as model-deviating item correlations. The method is also used as a goodness-of-fit test for whole data sets where the item parameters are assumed to be known. For tests with 30 items at most, exact values are obtained, for longer tests a Monte Carlo-algorithm is proposed. Simulated examples and an empirical investigation demonstrate test power and applicability to item elimination.The author wishes to thank Elisabeth Ponocny-Seliger and the reviewers for many helpful comments. All exact goodness-of-fit tests proposed in this article are implemented in the menu-driven program T-Rasch 1.0 by Ponocny and Ponocny-Seliger (1999) which can be obtained from ProGAMMA (WWW: http://www.gamma.rug.nl) and also performs nonparametric tests.     

A hierarchical latent response model for inferences about examinee engagement in terms of guessing and item-level non-response

In low-stakes assessments, test performance has few or no consequences for examinees themselves, so that examinees may not be fully engaged when answering the items. Instead of engaging in solution behaviour, disengaged examinees might randomly guess or generate no response at all. When ignored, examinee disengagement poses a severe threat to the validity of results obtained from low-stakes assessments. Statistical modelling approaches in educational measurement have been proposed that account for non-response or for guessing, but do not consider both types of disengaged behaviour simultaneously. We bring together research on modelling examinee engagement and research on missing values and present a hierarchical latent response model for identifying and modelling the processes associated with examinee disengagement jointly with the processes associated with engaged responses. To that end, we employ a mixture model that identifies disengagement at the item-by-examinee level by assuming different data-generating processes underlying item responses and omissions, respectively, as well as response times associated with engaged and disengaged behaviour. By modelling examinee engagement with a latent response framework, the model allows assessing how examinee engagement relates to ability and speed as well as to identify items that are likely to evoke disengaged test-taking behaviour. An illustration of the model by means of an application to real data is presented.     

“We” are Different: Exploring the Diverse Effects of Friend and Family Accessibility on Consumers' Product Preferences

Prior research suggests that close friends and family members exert similar effects on consumer behavior because both represent strong social ties and are subject to communal norms. However, drawing on regulatory focus theory, we postulate that accessibility of friend and family can have divergent impacts on consumers' subsequent purchase decisions. Across four experiments, as well as a pilot study, we demonstrate that accessibility of friend (vs. family) is more likely to activate a promotion focus, which results in more favorable consumer responses toward products with promotion‐focused appeals, whereas accessibility of family (vs. friend) is more likely to activate a prevention focus, which leads to more positive consumer responses toward products with prevention‐focused appeals.     

等效性检验——结构方程模型评价和测量不变性分析的新视角

常用的结构方程模型拟合指数存在一定局限, 如χ ²以传统零假设为目标假设, 无法验证模型, 而RMSEA和CFI等描述性的拟合指数不具备推断统计性质, 等效性检验有效弥补了这些问题。首先说明等效性检验如何评价单个模型的拟合, 并解释其与零假设检验的不同, 然后介绍等效性检验如何分析测量不变性, 接着用实证数据展示了等效性检验在单个模型评价和测量不变性检验中的效果, 并与传统模型评价方法比较。     

The asymmetric effect of regulatory fit on moral judgments of other-oriented lies

We examined whether regulatory fit effects are asymmetric—namely, whether they occur only among individuals with a promotion focus or a prevention focus. We adopted a task where individuals make moral judgments of other-oriented lies and conducted three studies. The results indicated that prevention-focused individuals judged other-oriented lies based on a vigilant strategy as more moral than lies based on an eager strategy (Studies 1 and 2). Meanwhile for promotion-focused individuals, there were no differences between eager and vigilant strategies on moral judgments of other-oriented lies. Additionally, the results suggested that the feeling of rightness is an underlying mechanism of the regulatory fit effects of prevention focus (Study 3).     

The Effects of Regulatory Fit between Explanation Framing and Applicants’ Regulatory Foci on Applicant Reaction

Abstract

Drawing from regulatory fit theory and the literature on persuasion, the current study is the first to explore whether the fit between explanation framing and applicants’ regulatory foci could enhance applicant reaction. We hypothesized that a positively framed explanation fits with applicants’ promotion foci and that a negatively framed explanation fits with applicants’ prevention foci. Three studies were conducted in which participants with different regulatory foci rated their perceived procedural fairness and organizational attractiveness after reading differently framed recruitment advertisements, rejection letters, and job offer letters. The results supported our hypothesis by showing significant interactions between explanation framing and participants’ regulatory foci on procedural fairness and organizational attractiveness perception in the contexts of recruitment advertising and rejection letters. In these contexts, compared with receiving a negatively framed explanation, promotion-focused recipients reported higher levels of perceived fairness and organizational attractiveness after receiving a positively framed explanation, and promotion-focused recipients’ fairness and attractiveness perceptions were higher than prevention-focused recipients’, after receiving a positively framed explanation. Moreover, perceived procedural fairness mediated the relationship between regulatory fit and perceived organizational attractiveness. However, regulatory fit effects were not found in the context of job offer letters.     

MCMC estimation and some model-fit analysis of multidimensional IRT models

A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization of the procedure to a model with multidimensional ability parameters are presented. The procedure is a generalization of a procedure by Albert (1992) for estimating the two-parameter normal ogive model. The procedure supports analyzing data from multiple populations and incomplete designs. It is shown that restrictions can be imposed on the factor matrix for testing specific hypotheses about the ability structure. The technique is illustrated using simulated and real data. The authors would like to thank Norman Verhelst for his valuable comments and ACT, CITO group and SweSAT for the use of their data.     

Asymptotic identifiability of nonparametric item response models

The identifiability of item response models with nonparametrically specified item characteristic curves is considered. Strict identifiability is achieved, with a fixed latent trait distribution, when only a single set of item characteristic curves can possibly generate the manifest distribution of the item responses. When item characteristic curves belong to a very general class, this property cannot be achieved. However, for assessments with many items, it is shown that all models for the manifest distribution have item characteristic curves that are very near one another and pointwise differences between them converge to zero at all values of the latent trait as the number of items increases. An upper bound for the rate at which this convergence takes place is given. The main result provides theoretical support to the practice of nonparametric item response modeling, by showing that models for long assessments have the property of asymptotic identifiability. The research was partially supported by the National Institute of Health grant R01 CA81068-01.     

Identifiability of nonlinear logistic test models

The linear logistic test model (LLTM) specifies the item parameters as a weighted sum of basic parameters. The LLTM is a special case of a more general nonlinear logistic test model (NLTM) where the weights are partially unknown. This paper is about the identifiability of the NLTM. Sufficient and necessary conditions for global identifiability are presented for a NLTM where the weights are linear functions, while conditions for local identifiability are shown to require a model with less restrictions. It is also discussed how these conditions are checked using an algorithm due to Bekker, Merckens, and Wansbeek (1994). Several illustrations are given.This article was written while the first author was a post doctoral fellow at the university of Twente. He gratefully acknowledges the university's hospitality and the financial support by NWO (project nr. 30002).     

Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models, unfolding models, latent class models with random effects, multilevel latent class models, models with log-normal latent variables, and zero-inflated Poisson models with random effects. Some of the ideas are illustrated by estimating an unfolding model for attitudes to female work participation. We wish to thank The Research Council of Norway for a grant supporting our collaboration.