Bayesian analysis of order-statistics models for ranking data

In this paper, a class of probability models for ranking data, the order-statistics models, is investigated. We extend the usual normal order-statistics model into one where the underlying random variables follow a multivariate normal distribution. Bayesian approach and the Gibbs sampling technique are used for parameter estimation. In addition, methods to assess the adequacy of model fit are introduced. Robustness of the model is studied by considering a multivariate-t distribution. The proposed method is applied to analyze the presidential election data of the American Psychological Association (APA).The author is grateful to K. Lam, K.F. Lam, the Editor, an associate editor, and three reviewers for their valuable comments and suggestions. This research was substantially supported by the CRCG grant 335/017/0015 of the University of Hong Kong and a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. HKU 7169/98H). Upon completion of this paper, I became aware that similar work had been done independently by K.G. Yao and U. Böckenholt (1999).     

A marginalization model for the multidimensional unfolding analysis of ranking data

A marginalization model for the multidimensional unfolding analysis of ranking data is presented. A subject samples one of a number of random points that are multivariate normally distributed. The subject perceives the distances from the point to all the stimulus points fixed in the same multidimensional space. The distances are error perturbed in this perception process. He/she produces a ranking dependent on these error-perturbed distances. The marginal probability of a ranking is obtained according to this ranking model and by integrating out the subject (ideal point) parameters, assuming the above distribution. One advantage of the model is that the individual differences are captured using the posterior probabilities of subject points. Three sets of ranking data are analyzed by the model.     

Modelling heterogeneity in behavioral response to peak-avoidance policy utilizing naturalistic data of Beijing subway travelers

Studies of travelers’ response behavior to transportation demand management is receiving substantial attention among researchers and transport operators in recent years. While previous studies in this area have generally assumed that the sensitivity of travelers to different factors is homogeneous and relies on survey responses, which may be prone to self-reporting errors and/or subject to behavioral incongruence. Relying on naturalistic data, this paper aims to investigate the behavioral response to pre-peak discount pricing strategy in the context of the Beijing subway with a special focus on the heterogeneity among the travelers. Anonymous smart card data from 5946 travelers before and after the introduction of a peak avoidance policy in Beijing are used to construct a latent class choice model to capture the sensitivity to different factors and the associated taste heterogeneity of travelers. Given the passive nature of the data, the model can offer more realistic outputs. The results indicate that there is substantial heterogeneity in travelers’ responses to the peak avoidance policy, and that they can be probabilistically allocated to four latent classes. For all classes of travelers, the decision to shift their departure to off-peak is affected by the monetary saving, the required change in departure time and the frequency of travel, but in different magnitudes. In particular, only two classes of travelers (who exhibit lower standard-deviation in pre-intervention departure time) show significant sensitivity to price changes indicating that the discount policies are more likely to be effective for these groups. The rest of travelers are largely price insensitive – warranting the need for non-monetary incentives as opposed to fare discounts. To the best of our knowledge, this study is the first to innovatively apply the LCC framework to analyze travelers’ heterogeneous behavior using large-scale smart card data without socio-demographic information. The findings can provide guidance to the subway authority in devising differential peak avoidance policies targeted for different groups of users, which are likely to be more effective than the current ‘one size fits all’ approach.     

A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

In the distance approach to nonlinear multivariate data analysis the focus is on the optimal representation of the relationships between the objects in the analysis. In this paper two methods are presented for including weights in distance-based nonlinear multivariate data analysis. In the first method, weights are assigned to the objects while the second method is concerned with differential weighting of groups of variables. When each analysis variable defines a group the latter method becomes a variable weighting method. For objects the weights are assumed to be given; for groups of variables they may be given, or estimated. These weighting schemes can also be combined and have several important applications. For example, they make it possible to perform efficient analyses of large data sets, to use the distance-based variety of nonlinear multivariate data analysis as an addition to loglinear analysis of multiway contingency tables, and to do stability studies of the solutions by applying the bootstrap on the objects or the variables in the analysis. These and other applications are discussed, and an efficient algorithm is proposed to minimize the corresponding loss function.This study is funded by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the PIONEER project Subject Oriented Multivariate Analysis to the third author.     

Risk preferences in young children: early evidence of individual differences in reaction to potential gains and losses

New methods were developed for studying risky decision making in children as young as age five. Each child was given a block of ‘gain’ trials, for example, a choice between a sure gain of one prize and a 50:50 chance of gaining either two prizes or no prize, and a block of ‘loss’ trials, for example, a choice between a sure loss of one prize and a 50:50 chance of losing either two prizes or no prize. We were thus able to compare risky choice for gains and losses at the level of the individual child. In each of two experiments a variety of individual difference variables were measured, including in Experiment 2, the child's parent's scores on the same task. Across experiments, the preponderance of choices was of the risky option. However, most children and adults made more risky choices in the domain of losses than in the domain of gains. Predictors of individual differences in children included shyness, impulsivity, and the risk taking of the child's parent. We suggest that methods are now in place to encourage further studies of decision processes in young children. Copyright © 2003 John Wiley & Sons, Ltd.     

Assessment of generalised Bayesian structural equation models for continuous and binary data

The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive $p$-values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17 , 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.     

The Creativity Matrix: Spotlights and Blind Spots in Our Understanding of the Phenomenon

In this paper, we organize past and present theories and models of creativity by using a new conceptual framework—the creativity matrix—with the aim of highlighting the dimensions of creativity we know a lot about and those we tend to either ignore or find difficult to study. This matrix is formed by bringing together a developmental model of creativity (the 4 C's) and a structural one (the 5 A's). We start by briefly describing these two conceptual frameworks, and then, we proceed to exploring the matrix itself by describing how the 5 A's are dynamically organized at each “level” of the 4 C's. Importantly, our overview of the matrix is informed by existing models and concepts that address one of more of the C's and the A's. This gives us a unique opportunity to take stock of what has been studied so far and, toward the end, consider new avenues for the development of theory and research agendas within creativity studies.     

Hierarchical classes models for three-way three-mode binary data: interrelations and model selection

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans, Van Mechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.     

Optimal scaling of paired comparison and rank order data: An alternative to guttman's formulation

A formulation, which is different from Guttman's is presented. The two formulations are both called the optimal scaling approach, and are proven to provide identical scale values. The proposed formulation has at least two advantages over Guttman's. Namely, (i) the former serves to clarify close relations of the optimal scaling approach to those of Slater and the vector model of preferential choice, and (ii) in addition to the stimulus scale values, it provides scores for the subjects, which indicate the degrees of response consistency (transitivity), relative to the optimum solution. The method is assumption-free and capable of multidimensional analysis.This study was partly supported by the National Research Council Grant (No. A4581) to S. Nishisato. The author is indebted to Dr. Bert F. Green, Jr., Mr. Tomoichi Ishizuka, and anonymous reviewers for their valuable comments on an earlier draft.     

Development of specialization scales for the MSPI: A comparison of empirical and inductive strategies

An empirical measurement model for interest inventory construction uses internal criteria whereas an inductive measurement model uses external criteria. The empirical and inductive measurement models are compared and contrasted and then two models are assessed through tests of the effectiveness and economy of scales for the Medical Specialty Preference Inventory (Zimney, 1979). The empirical results clearly demonstrate the advantages of using an empirical model for occupational interest inventory construction, whether alone or in conjunction with an inductive model. Furthermore, the results indicated that the empirical model may be used to resolve the long-standing problems in constructing predictive inventories for specialty choice within an occupation.     

Selecting polychoric instrumental variables in confirmatory factor analysis: An alternative specification test and effects of instrumental variables

The polychoric instrumental variable (PIV) approach is a recently proposed method to fit a confirmatory factor analysis model with ordinal data. In this paper, we first examine the small-sample properties of the specification tests for testing the validity of instrumental variables (IVs). Second, we investigate the effects of using different numbers of IVs. Our results show that specification tests derived for continuous data are extremely oversized at all sample sizes when applied to ordinal variables. Possible modifications for ordinal data are proposed in the present study. Simulation results show that the modified specification tests with all available IVs are able to detect model misspecification. In terms of estimation accuracy, the PIV approach where the IVs outnumber the endogenous variables by one produces a lower bias but a higher variation than the PIV approach with more IVs for correctly specified factor loadings at small samples.     

Testing two variances for superiority/non-inferiority and equivalence: Using the exhaustion algorithm for sample size allocation with cost

The equality of two group variances is frequently tested in experiments. However, criticisms of null hypothesis statistical testing on means have recently arisen and there is interest in other types of statistical tests of hypotheses, such as superiority/non-inferiority and equivalence. Although these tests have become more common in psychology and social sciences, the corresponding sample size estimation for these tests is rarely discussed, especially when the sampling unit costs are unequal or group sizes are unequal for two groups. Thus, for finding optimal sample size, the present study derived an initial allocation by approximating the percentiles of an F distribution with the percentiles of the standard normal distribution and used the exhaustion algorithm to select the best combination of group sizes, thereby ensuring the resulting power reaches the designated level and is maximal with a minimal total cost. In this manner, optimization of sample size planning is achieved. The proposed sample size determination has a wide range of applications and is efficient in terms of Type I errors and statistical power in simulations. Finally, an illustrative example from a report by the Health Survey for England, 1995–1997, is presented using hypertension data. For ease of application, four R Shiny apps are provided and benchmarks for setting equivalence margins are suggested.     

Psychometric properties and reference data for Danish versions of Free and Cued Selective Reminding Test,Category Cued Memory Test and Logical Memory

Memory assessment is a key element in neuropsychological testing. Gold standard evaluation is based on updated normative data, but in many small countries (e.g. in Scandinavia) such data are sparse. In Denmark, reference data exist for non‐verbal memory tests and list‐learning tests but there is no normative data for memory tests which capture narrative recall and cued recall. In a nation‐wide study, Free and Cued Selective Reminding Test (FCSRT ), WMS ‐III Logical Memory (LM ) and a newly developed test Category Cued Memory Test (CCMT ‐48) were applied in 131 cognitively intact persons (aged 60–96 years). Regression‐based reference data for Danish versions of FCSRT , CCMT ‐48 and LM adjusted for age, education and gender are provided. Gender and age‐group had a significant impact on the expected scores, whereas the effect of education had a limited effect on expected scores. Test performances were significantly correlated in the range 0.21–0.51. Based on these findings and previous results it may be relevant to assess both free recall, cued recall and recognition to tap the earliest changes associated with neurodegeneration, and this study therefore provides an important supplement to existing Danish normative data. Future studies should investigate the discriminative validity of the tests and the clinical utility of the presented reference data.     

Convergence properties of an iterative procedure of ipsatizing and standardizing a data matrix,with applications to parafac/candecomp preprocessing

Centering a matrix row-wise and rescaling it column-wise to a unit sum of squares requires an iterative procedure. It is shown that this procedure converges to a stable solution. This solution need not be centered row-wise if the limiting point of the interations is a matrix of rank one. The results of the present paper bear directly on several types of preprocessing methods in Parafac/Candecomp.     

问题解决测验中过程数据的特征抽取与能力评估

基于计算机的问题解决测验可以实时记录被试探索环境和解决问题时的详细行动痕迹,并保存为过程数据。首先介绍了过程数据的分析流程,然后从问题解决测验入手,分别对过程数据的特征抽取和能力估计建模两方面的研究进行了梳理和评价。未来研究应注意：提高分析结果的可解释性;特征提取时纳入更多信息;实现更复杂问题情景下的能力评估;注重方法的实用性;以及融合与借鉴不同领域的分析方法。     

A Spanish translation of Mehrabian and Russell's emotionality scales for environmental consumer psychology

The development of a translation of Mehrabian and Russell's scales for the measurement of pleasure, arousal and dominance from the original English to a Spanish version for use in Venezuela is described. The translated scales were administered to two samples of middle‐class Venezuelan consumers (n = 127,n = 127) between the ages of 20 and 50, among whom males and females were represented approximately equally. Internal reliability (measured by Cronbach's alpha) and scale validity (measured by factor analysis) indicate that the translated scales are suitable for consumer and other social psychological research in Spanish. Copyright © 2002 Henry Stewart Publications Ltd.     

Exercise Training for Persons with Alzheimer's Disease and Caregivers: A Review of Dyadic Exercise Interventions

Alzheimer's disease (AD) is the most common form of dementia and the prevalence will increase dramatically in the next decades. Although exercise has shown benefits for people with dementia due to AD as well as their caregivers, the impact of a dyadic exercise intervention including both groups as study participants remains to be determined. The authors review the current clinical evidence for dyadic exercise interventions, which are exercise regimens applied to both the person with dementia and the caregiver. A total of 4 controlled trials were reviewed. This review shows that dyadic exercise interventions are feasible and may produce a positive effect on functional independence and caregiver burden. However, there was insufficient evidence to support a benefit of dyadic exercise intervention on cognitive performance and on behavioral and neuropsychiatric symptoms in participants with dementia due to AD. A dyadic exercise intervention improves functional independence and caregiver burden. However, there is a need for well-designed randomized controlled clinical trials to confirm these benefits and to investigate several important points such as the effects of a dyadic exercise intervention on cognitive and noncognitive outcomes of AD, the optimal intensity of exercise training, and the cost effectiveness of such a program.     

The Protective Functions of Social Support and Coping for Women Experiencing Intimate Partner Abuse

This study evaluated two alternate models exploring protective factors in the relationship between intimate partner abuse and health: one in which social support was proposed to mediate the violence-health relation, and a second in which coping was proposed to mediate this relation, while social support would moderate the abuse-coping relation. Women were administered questionnaires measuring coping, social support, violence, and health status. Relationship violence predicted mental health status only, although mental health did predict physical health. Coping was found to serve as a mediator between abuse and health. Implications for future research and clinical applications are discussed.