Psychometric latent response models

In this paper, some psychometric models will be presented that belong to the larger class oflatent response models (LRMs). First, LRMs are introduced by means of an application in the field ofcomponential item response theory (Embretson, 1980, 1984). Second, a general definition of LRMs (not specific for the psychometric subclass) is given. Third, some more psychometric LRMs, and examples of how they can be applied, are presented. Fourth, a method for obtaining maximum likelihood (ML) and some maximum a posteriori (MAP) estimates of the parameters of LRMs is presented. This method is then applied to theconjunctive Rasch model. Fifth and last, an application of the conjunctive Rasch model is presented. This model was applied to responses to typical verbal ability items (open synonym items).This paper presents theoretical and empirical results of a research project supported by the Research Council [Onderzoeksraad] of the University of Leuven (grant number 89-9) to Paul De Boeck and Luc Delbeke.     

Trust and the collection,selection, analysis and interpretation of data: A scientist’s view

Trust is a critical component of research: trust in the work of co-workers and colleagues within the scientific community; trust in the work of research scientists by the non-research community. A wide range of factors, including internally and externally generated pressures and practical and personal limitations, affect the research process. The extent to which these factors are understood and appreciated influence the development of trust in scientific research findings.     

Rasch's model for reading speed with manifest explanatory variables

In educational and psychological measurement we find the distinction between speed and power tests. Although most tests are partially speeded, the speed element is usually neglected. Here we consider a latent trait model developed by Rasch for the response time on a (set of) pure speed test(s), which is based on the assumption that the test response times are approximately gamma distributed, with known shape parameters and scale parameters depending on subject ability and test difficulty parameters. In our approach the subject parameters are treated as random variables having a common gamma distribution. From this, maximum marginal likelihood estimators are derived for the test difficulties and the parameters of the latent subject distribution. This basic model can be extended in a number of ways. Explanatory variables for the latent subject parameters and for the test parameters can be incorporated in the model. Our methods are illustrated by the analysis of a simulated and an empirical data set.     

A multidimensional latent trait model for measuring learning and change

A latent trait model is presented for the repeated measurement of ability based on a multidimensional conceptualization of the change process. A simplex structure is postulated to link item performance under a given measurement condition or occasion to initial ability and to one or more modifiabilities that represent individual differences in change. Since item discriminations are constrained to be equal within a measurement condition, the model belongs to the family of multidimensional Rasch models. Maximum likelihood estimators of the item parameters and abilities are derived, and an example provided that shows good recovery of both item and ability parameters. Properties of the model are explored, particularly for several classical issues in measuring change.     

Easy bayes estimation for rasch-type models

A Bayes estimation procedure is introduced that allows the nature and strength of prior beliefs to be easily specified and modal posterior estimates to be obtained as easily as maximum likelihood estimates. The procedure is based on constructing posterior distributions that are formally identical to likelihoods, but are based on sampled data as well as artificial data reflecting prior information. Improvements in performance of modal Bayes procedures relative to maximum likelihood estimation are illustrated for Rasch-type models. Improvements range from modest to dramatic, depending on the model and the number of items being considered.This research was supported by ORN Contact #00014-86-K0087. We wish to thank Sheng-Hui Chu and Dzung-Ji Lii for providing intelligent and energetic programming support for this article. We also thank one of the reviewers for pointing out several interesting and useful perspectives.     

The essential process in a family of measurement models

Five members of the Rasch family of latent trait models which have appeared more or less independently in the literature are brought together and identified as one model. In addition to sharing the distinguishing characteristic of the dichotomous Rasch model—separable person and item parameters and hence sufficient statistics—all five models share a common algebraic form and have as their basic element the fundamental process defined by Rasch's simple logistic expression. In these models, the sufficient statistics for person and item parameters are counts of events constructed to be indicative of the variable being measured, and the measures they enable are ‘fundamental’.     

Marginal maximum likelihood estimation for the one-parameter logistic model

Two algorithms are described for marginal maximum likelihood estimation for the one-parameter logistic model. The more efficient of the two algorithms is extended to estimation for the linear logistic model. Numerical examples of both procedures are presented. Portions of this research were presented at the meeting of the Psychometric Society in Chapel Hill, N.C. in May, 1981. Thanks to R. Darrell Bock, Gerhard Fischer, and Paul Holland for helpful comments in the course of this research.     

Traiter ses données proprement : vers un meilleur usage du data cleaning

Data cleaning has long been shown to be an essential step in data analysis but its application is not systematic and varies between researchers. The aim of the present article is to study the effect of the different elements of data cleaning and to give recommendations. The steps considered are normality, outliers and missing values. Firstly, we advise a close examination of the data and its normality with a view to transformation. Secondly, we look at outliers and their treatment (trimming and winsorizing). Lastly, we turn our attention to missing values and how to deal with them depending on their randomness. We also cover the importance of visualizing data before analysis. Finally, we illustrate the effect of these data cleaning practices on a real data set. We show that data cleaning can lead to a significant result becoming non-significant and therefore demonstrate the importance of these steps before starting data analysis.     

A subculture of mental toughness in an Australian Football League club

ObjectiveThe current study aimed to provide a subcultural analysis of mental toughness in a high-performance context in sport.DesignUsing Schein's (1990) framework of organisational culture, an exploratory qualitative analysis, employing focus group and individual interviews, was used to investigate mental toughness in an Australian Football League club.MethodNine senior coaches and players participated in focus group and individual interviews. Photo elicitation was used as a method to capture mental toughness through the identification of prominent club artefacts. Participants were considered to have significant subcultural knowledge of their football club and were willing to describe personal experiences and perceptions of mental toughness through this cultural lens. Deductive and inductive analyses were conducted to capture the core themes of mental toughness across the disparate levels of Schein's organisational framework.ResultsMental toughness was found to be a socially derived term marked by unrelenting standards and sacrificial displays. These acts were underpinned by subcultural values emphasising a desire for constant improvement, a team first ethos, relentless effort, and the maintenance of an infallible image. At its core, mental toughness was assumed to be an internal concept, epitomised an idealised form of masculinity, elitist values, and was rhetorically depicted through metaphors of war.ConclusionsIt may be difficult to understand mental toughness without giving attention to the contextual norms related to the term. Appreciating how people promote, instil, and internalise prized ideals coveted as mental toughness could be intriguing for future research in sport psychology.     

探测认知发展的非连续性:思路与方法

认知发展是连续性的还是非连续性的是发展心理学中一个重要理论问题.目前研究者逐渐认识到发展既有连续性的也有非连续性的,因而关键的问题是什么情况下是非连续性的,什么情况下是连续性的.该问题的回答与非连续性的检测标准有很大关系.本研究详细介绍了考察发展非连续性的三种思路和方法:量表图分析和多重任务法;Rasch模型分析和Saltus模型分析;突变理论的应用.最后,强调了影响发展非连续性问题探讨的一些因素.