Personality and Integrative Negotiations: A HEXACO Investigation of Actor,Partner, and Actor–Partner Interaction Effects on Objective and Subjective Outcomes

The present study sought to expand the literature on the relations of major dimensions of personality with integrative negotiation outcomes by introducing the HEXACO model, investigating both effects of the negotiators' and their counterparts' personality traits on objective and subjective negotiation outcomes, and investigating two interactions between the negotiators' and counterparts' personalities. One hundred forty‐eight participants completed the HEXACO‐100 measure of personality. Participants then engaged in a dyadic negotiation task that contained a mix of distributive and integrative elements (74 dyads). Measures of subjective experience and objective economic value were obtained, and actor–partner interdependence models were estimated. Personality was generally a better predictor of subjective experience than objective economic value. In particular, partner honesty‐humility, extraversion, and openness predicted more positive negotiation experiences. An actor–partner interaction effect was found for actor‐agreeableness by partner‐honesty‐humility on economic outcomes; agreeable actors achieved worse (better) economic outcomes when negotiating with partners that were low (high) on honesty‐humility. © 2018 European Association of Personality Psychology     

The Role of Independence and Stationarity in Probabilistic Models of Binary Choice

After more then 50 years of probabilistic choice modeling in economics, marketing, political science, psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within‐respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties. Copyright © 2017 John Wiley & Sons, Ltd.     

A Bayesian analysis of finite mixtures in the LISREL model

In this paper, we propose a Bayesian framework for estimating finite mixtures of the LISREL model. The basic idea in our analysis is to augment the observed data of the manifest variables with the latent variables and the allocation variables. The Gibbs sampler is implemented to obtain the Bayesian solution. Other associated statistical inferences, such as the direct estimation of the latent variables, establishment of a goodness-of-fit assessment for a posited model, Bayesian classification, residual and outlier analyses, are discussed. The methodology is illustrated with a simulation study and a real example.This research was supported by a Hong Kong UGC Earmarked grant CUHK 4026/97H. The authors are indebted to the Editor, the Associate Editor, and three anonymous reviewers for constructive comments in improving the paper, and also to ICPSR and the relevant funding agency for allowing the use of the data. The assistance of Michael K.H. Leung and Esther L.S. Tam is gratefully acknowledged.     

Bayesian inference for graphical factor analysis models

We generalize factor analysis models by allowing the concentration matrix of the residuals to have nonzero off-diagonal elements. The resulting model is named graphical factor analysis model. Allowing a structure of associations gives information about the correlation left unexplained by the unobserved variables, which can be used both in the confirmatory and exploratory context. We first present a sufficient condition for global identifiability of this class of models with a generic number of factors, thereby extending the results in Stanghellini (1997) and Vicard (2000). We then consider the issue of model comparison and show that fast local computations are possible for this purpose, if the conditional independence graphs on the residuals are restricted to be decomposable and a Bayesian approach is adopted. To achieve this aim, we propose a new reversible jump MCMC method to approximate the posterior probabilities of the considered models. We then study the evolution of political democracy in 75 developing countries based on eight measures of democracy in two different years. We acknowledge support from M.U.R.S.T. of Italy and from the European Science Foundation H.S.S.S. Network. We are grateful to the referees and the Editor for many useful suggestions and comments which led to a substantial improvement of the paper. We also thank Nanny Wermuth for stimulating discussions and Kenneth A. Bollen for kindly providing us with the data-set.     

Spatial reconstruction of human motion by means of a single camera and a biomechanical model

The value of the results of the inverse dynamic analysis procedures used in the study of human tasks is dependent on the quality of the kinematic and kinetic data supplied to the biomechanical model that supports it. The kinematic data, containing the position, velocity and acceleration of all anatomical segments of the biomechanical model, result from the reconstruction of human spatial motion by means of the evaluation of the anatomic points positions that enable to uniquely define the position of all anatomical segments. Furthermore, the motion data must be kinematically consistent with the structure of the biomechanical model used in the analysis. The traditional photogrammetric methodologies used for the spatial reconstruction of the human motion require images of two or more calibrated and synchronized cameras. This is due to the fact that the projection of each anatomical point is described by two linear equations relating its three spatial coordinates with the two coordinates of the projected point. The need for the image of another camera arises from the fact that a third equation is necessary to find the original spatial position of the anatomical point. The methodology proposed here substitutes the projection equations of the second camera with the kinematic constraint equations associated with a biomechanical model in the motion reconstruction process. In the formulation the system of equations arising from the point projections and biomechanical model kinematic constraints, representing the constant length of the anatomical segments, are solved simultaneously. Because the system of equations has multiple solutions for each image, a strategy based on the minimization of a cost function associated to the smoothness of the reconstructed motion is devised. It is shown how the process is implemented computationally avoiding any operator intervention during the motion reconstruction for a given time period. This leads to an automated computer procedure that ensures the uniqueness of the reconstructed motion. The result of the reconstruction process is a set of data that is kinematically consistent with the biomechanical model used. Through applications of the proposed methodology to several sports exercises its benefits and shortcomings are discussed.     

结构方程在心理学研究中的应用

近年来,在教育、社会及心理学等研究领域,结构方程模型(SEM,Structural Equation Modeling)已成为重要的多变量分析方法,并日益受到广大研究者的重视。本文旨在对SEM作简要概述,并讨论其在心理学研究中的应用,以及在应用过程中应该遵循的标准和需要注意的一些问题。     

Evaluating Three Models of Intimate Relationships

The purpose of this study was to evaluate three models of intimate relationships: attachment, elementary pragmatic, and selfhood. These models were evaluated with three instruments respectively constructed to evaluate the validity of each model. One convenience group of functioning individuals and three experimental groups of inpatients and outpatients were administered the three instruments. All three instruments showed relatively high internal consistency and discriminated among the four groups. Significant correlations were found among the measure of attachment with measures of the pragmatic elementary and selfhood models. These correlations indicate that all three instruments were measuring similar aspects of intimate relationships. Clinical and preventive implications and applications of these models are briefly discussed.     

Minimax d-optimal designs for item response theory models

Various different item response theory (IRT) models can be used in educational and psychological measurement to analyze test data. One of the major drawbacks of these models is that efficient parameter estimation can only be achieved with very large data sets. Therefore, it is often worthwhile to search for designs of the test data that in some way will optimize the parameter estimates. The results from the statistical theory on optimal design can be applied for efficient estimation of the parameters.A major problem in finding an optimal design for IRT models is that the designs are only optimal for a given set of parameters, that is, they are locally optimal. Locally optimal designs can be constructed with a sequential design procedure. In this paper minimax designs are proposed for IRT models to overcome the problem of local optimality. Minimax designs are compared to sequentially constructed designs for the two parameter logistic model and the results show that minimax design can be nearly as efficient as sequentially constructed designs.