Assessing item-feature effects with item response tree models

Recent applications of item response tree models demonstrate that this model class is well suited to detect midpoint and extremity response style effects in both attitudinal and personality measurements. This paper proposes an extension of this approach that goes beyond measuring response styles and allows us to examine item-feature effects. In a reanalysis of three published data sets, it is shown that the proposed extension captures item-feature effects across affirmative and reverse-worded items in a psychological test. These effects are found to affect directional responses but not midpoint and extremity preferences. Moreover, accounting for item-feature effects substantially improves model fit and interpretation of the construct measurement. The proposed extension can be implemented readily with current software programs that facilitate maximum likelihood estimation of item response models with missing data.     

On scaling models applied to data from several groups

Statistical methods are presented to facilitate a more complete analysis of results obtained when a scaling model is applied to data from two or more groups. These methods can be used to (a) compare the corresponding estimated latent distributions obtained using the scaling model applied to the different groups, (b) compare the corresponding estimated item reliabilities (or item response error rates) for the different groups, and (c) test whether the scaling model applied to the several groups can be replaced by a more parsimonious scaling model that includes various homogeneity constraints (i.e., constraints that describe which parameters in the model are the same for the several groups). Various kinds of scaling models are considered here in the multiple-group context.Support for this research was provided in part by the National Science Foundation, to Clogg by Grant No. SES-7823759 and to Goodman by Grant No. SES-8303838. Clogg and Goodman were Fellows at the Center for Advanced Study in the Behavioral Sciences when part of the research was done, with financial support provided in part by National Science Foundation grant BNS-8011494 to the Center. The authors are indebted to Mark P. Becker and James W. Shockey for helpful comments.     

Evaluating the reliance on past choices in adaptive learning models

Adaptive learning models are used to predict behavior in repeated choice tasks. Predictions can be based on previous payoffs or previous choices of the player. The current paper proposes a new method for evaluating the degree of reliance on past choices, called equal payoff series extraction (EPSE). Under this method a simulated player has the same exact choices as the player but receives equal constant payoffs from all of the alternatives. Success in predicting the next choice ahead for this simulated player therefore relies strictly on mimicry of previous choices of the actual player. This allows determining the marginal fit of predictions that are not based on the actual task payoffs. To evaluate the reliance on past choices under different models, an experiment was conducted in which 48 participants completed a three-alternative choice task in four task conditions. Two different learning rules were evaluated: an interference rule and a decay rule. The results showed that while the predictions of the decay rule relied more on past choices, only the reliance on past payoffs was associated with improved parameter generality. Moreover, we show that the Equal Payoff Series can be used as a criterion for optimizing parameters resulting in better parameter generalizability.     

Assessing the driving distraction effect of vehicle HMI displays using data mining techniques

With the rapid development of human–machine interface (HMI) systems in vehicles, driving distraction caused by HMI displays affects road safety. This study presents a data mining technique to model the four driving distraction indicators: speed deviation, lane departure standard deviation, dwell time, and mean glance time. Driving distraction data was collected on a real-car driving simulator. 3 secondary tasks in 13 mass produced cars were tested by 24 drivers. The random forest algorithm outperformed linear regression, extreme gradient boosting, and multi-layer perceptron as the best model, demonstrating good regression performance as well as good interpretability. The result of random forest showed that the importance of target speed is large for all driving distraction indicators. Among the variables of interaction and user interface design, less step and less on-screen distance of finger movement are efficient for lowering lane departure standard deviation and dwell time. The position of right point is another important variable, and should be between 37 and 47 degrees on a typical sample in this study. A larger angle leads to bigger lane departure, while a smaller angle leads to bigger mean glance time. Most variables of HMI display positioning themselves are not important. This study provides one driving distraction assessment method with a variable impact trend analysis for HMI secondary tasks in an early phase of product development.     

Fictionalism and the informativeness of identity

Identity claims often look nonsensical because they apparently declaredistinct things to be identical. I argue that this appearance is notjust an artefact of grammar. We should be fictionalists about such claims,seeing them against the background of speakers' pretense that their wordssecure reference to a plurality of objects that are then declared to beidentical from within the pretense. I argue that it is the resultinginterpretative tension – arising from the fact that two things cannever be identical – that allows us to understand the real point ofsuch statements. This view also offers a new solution to Fege's puzzleof the informativeness of identity statements.     

Factor copula models for mixed data

We develop factor copula models to analyse the dependence among mixed continuous and discrete responses. Factor copula models are canonical vine copulas that involve both observed and latent variables, hence they allow tail, asymmetric and nonlinear dependence. They can be explained as conditional independence models with latent variables that do not necessarily have an additive latent structure. We focus on important issues of interest to the social data analyst, such as model selection and goodness of fit. Our general methodology is demonstrated with an extensive simulation study and illustrated by reanalysing three mixed response data sets. Our studies suggest that there can be a substantial improvement over the standard factor model for mixed data and make the argument for moving to factor copula models.     

E-Science and the data deluge

This paper attempts to show how the “big data” paradigm is changing science through offering access to millions of database elements in real time and the computational power to rapidly process those data in ways that are not initially obvious. In order to gain a proper understanding of these changes and their implications, we propose applying an extended cognition model to the novel scenario.     

A general approach to categorical data analysis with missing data,using generalized linear models with composite links

A general approach for analyzing categorical data when there are missing data is described and illustrated. The method is based on generalized linear models with composite links. The approach can be used (among other applications) to fill in contingency tables with supplementary margins, fit loglinear models when data are missing, fit latent class models (without or with missing data on observed variables), fit models with fused cells (including many models from genetics), and to fill in tables or fit models to data when variables are more finely categorized for some cases than others. Both Newton-like and EM methods are easy to implement for parameter estimation.The author thanks the editor, the reviewers, Laurie Hopp Rindskopf, and Clifford Clogg for comments and suggestions that substantially improved the paper.     

On growth curves and mixture models

The multilevel model of change and the latent growth model are flexible means to describe all sorts of population heterogeneity with respect to growth and development, including the presence of sub‐populations. The growth mixture model is a natural extension of these models. It comes at hand when information about sub‐populations is missing and researchers nevertheless want to retrieve developmental trajectories from sub‐populations. We argue that researchers have to make rather strong assumptions about the sub‐populations or latent trajectory classes in order to retrieve existing population differences. A simulated example is discussed, showing that a sample of repeated measures drawn from two sub‐populations easily leads to the mistaken inference of three sub‐populations, when assumptions are not met. The merits of methodological advises on this issue are discussed. It is concluded that growth mixture models should be used with understanding, and offer no free way to growth patterns in unknown sub‐populations. Copyright © 2006 John Wiley & Sons, Ltd.     

Assessing human reorientation ability inside virtual reality environments: the effects of retention interval and landmark characteristics

The purpose of the present study was to assess the navigational behaviour of adult humans following a disorientation procedure that perturbed their egocentric frame of reference. The assessment was carried out in a virtual reality (VR) environment by manipulating the disorientation procedure, the retention interval, the relative positions of target and landmark. The results of experiment I demonstrated that adding a physical rotation to a virtual disorientation procedure did not yield an additional decrease in searching performance. The results of experiment II showed that shortening the delay between study and test phase decreased the errors more markedly for geometric than landmark ones. An orientation specificity effect due to the manipulation of the relative position between target and landmark was discussed across the experiments. In conclusion, VR seemed to be a valuable method for studying human reorientation. Moreover, the virtual experimental setting involved here promoted knowledge of the relationship between working memory and spatial reorientation paradigm.     

基于个体的退休心理模型

退休是个体在特定年龄领取养老金的行为及动态过程。退休模型包括退休的影响因素模型、退休过程模型和退休结果模型三类。其中,退休的影响因素模型包括前因变量模型和退休多水平模型,分别从个人-环境和宏观角度提出了影响退休的各类因素;退休过程模型包括退休决策三阶段模型、退休时间过程模型和退休适应动态模型,分别从阶段性、个体差异性和动态性的角度揭示了退休过程的心理特点;退休结果模型分析了退休对个人和组织的影响。退休四阶段模型是在西方模型的基础上结合中国国情提出的一个整合模型。未来研究应丰富研究对象的类型、揭示影响退休的心理学变量的作用机制、加强应用性并进行本土化研究。     

Pairwise likelihood estimation for confirmatory factor analysis models with categorical variables and data that are missing at random

Methods for the treatment of item non-response in attitudinal scales and in large-scale assessments under the pairwise likelihood (PL) estimation framework and under a missing at random (MAR) mechanism are proposed. Under a full information likelihood estimation framework and MAR, ignorability of the missing data mechanism does not lead to biased estimates. However, this is not the case for pseudo-likelihood approaches such as the PL. We develop and study the performance of three strategies for incorporating missing values into confirmatory factor analysis under the PL framework, the complete-pairs (CP), the available-cases (AC) and the doubly robust (DR) approaches. The CP and AC require only a model for the observed data and standard errors are easy to compute. Doubly-robust versions of the PL estimation require a predictive model for the missing responses given the observed ones and are computationally more demanding than the AC and CP. A simulation study is used to compare the proposed methods. The proposed methods are employed to analyze the UK data on numeracy and literacy collected as part of the OECD Survey of Adult Skills.     

Polytomous IRT models and monotone likelihood ratio of the total score

In a broad class of item response theory (IRT) models for dichotomous items the unweighted total score has monotone likelihood ratio (MLR) in the latent trait. In this study, it is shown that for polytomous items MLR holds for the partial credit model and a trivial generalization of this model. MLR does not necessarily hold if the slopes of the item step response functions vary over items, item steps, or both. MLR holds neither for Samejima's graded response model, nor for nonparametric versions of these three polytomous models. These results are surprising in the context of Grayson's and Huynh's results on MLR for nonparametric dichotomous IRT models, and suggest that establishing stochastic ordering properties for nonparametric polytomous IRT models will be much harder.Hemker's research was supported by the Netherlands Research Council, Grant 575-67-034. Junker's research was supported in part by the National Institutes of Health, Grant CA54852, and by the National Science Foundation, Grant DMS-94.04438.     

Mathematical models and the experimental analysis of behavior

The use of mathematical models in the experimental analysis of behavior has increased over the years, and they offer several advantages. Mathematical models require theorists to be precise and unambiguous, often allowing comparisons of competing theories that sound similar when stated in words. Sometimes different mathematical models may make equally accurate predictions for a large body of data. In such cases, it is important to find and investigate situations for which the competing models make different predictions because, unless two models are actually mathematically equivalent, they are based on different assumptions about the psychological processes that underlie an observed behavior. Mathematical models developed in basic behavioral research have been used to predict and control behavior in applied settings, and they have guided research in other areas of psychology. A good mathematical model can provide a common framework for understanding what might otherwise appear to be diverse and unrelated behavioral phenomena. Because psychologists vary in their quantitative skills and in their tolerance for mathematical equations, it is important for those who develop mathematical models of behavior to find ways (such as verbal analogies, pictorial representations, or concrete examples) to communicate the key premises of their models to nonspecialists.     

Measuring data sharing intention and its association with the acceptance of connected vehicles

Transportation agencies and researchers are optimistic about the potential use of data collected from connected vehicles (CVs) for a variety of traffic and transportation applications. However, the literature lacks the evaluation of data sharing intention of the public for CV applications and its relationship with CV acceptance. This study investigated this gap by conducting a questionnaire survey of 2400 US adults. The results showed that the intention to share CV data depends upon the use of data but not the type of data. The possible uses of CV data were found to be grouped under four categories: driver information, congestion assessment and reduction, and pavement and infrastructure assessment and improvement (ICP); enforcement of traffic rules and fees based on usage (EF); roadside assistance and crash investigation (RC); and research purposes (RP). The data sharing intention for these four data uses vary, though with some commonality, which reflects the overall data sharing intention in CV technology (CVT). In addition, it was found that data privacy and security issues of CVT lower the data sharing intention and CV acceptance. Thus, a number of ways to improve CV acceptance by minimizing the data issues of CVT are discussed. Significant differences in perception of data privacy and security, data sharing intention, and CV acceptance were observed for individuals of different socio-economic and driving-related characteristics.     

Assessing the Relationship Between Differentiation and Infidelity

SUMMARY

Differentiation and infidelity have been theoretically related. However, few studies have tested whether the concepts in differentiation are statistically related to infidelity. One hundred nineteen participants in the present study completed two inventories assessing their level of differentiation and engaging in infidelity. Two-step structural equation modeling is used to determine the degree to which differentiation relates to infidelity. Results indicated that, though differentiation may be part of a plausible model of infidelity behavior, there are potentially other models that may be relevant in engaging in infidelity. The findings and implications are discussed.