Thurstonian modeling of ranking data via mean and covariance structure analysis

Although Thurstonian models provide an attractive representation of choice behavior, they have not been extensively used in ranking applications since only recently efficient estimation methods for these models have been developed. These, however, require the use of special-purpose estimation programs, which limits their applicability. Here we introduce a formulation of Thurstonian ranking models that turns an idiosyncratic estimation problem into an estimation problem involving mean and covariance structures with dichotomous indicators. Well-known standard solutions for the latter can be readily applied to this specific problem, and as a result any Thurstonian model for ranking data can be fitted using existing general purpose software for mean and covariance structure analysis. Although the most popular programs for covariance structure analysis (e.g., LISREL and EQS) cannot be presently used to estimate Thurstonian ranking models, other programs such as MECOSA already exist that can be straightforwardly used to estimate these models.This paper is based on the author's doctoral dissertation. Ulf Böckenholt was my advisor. The author is indebted to Ulf Böckenholt for his comments on a previous version of this paper and to Gerhard Arminger for his extensive support on the use of MECOSA. The final stages of this research took place while the author was at the Department of Statistics and Econometrics, Universidad Carlos III de Madrid. Conversations with my colleague there, Adolfo Hernández, helped to greatly improve this paper.     

Similarity and multidimensional ratio estimation with simultaneous qualitative and quantitative variation

Abstract.— Previous work on content models of multidimensional similarity have mostly been concerned with the case of only qualitative variation. In this study a set of emotion words that varied both in content and intensity was used as stimuli. Subjects gave multidimensional ratio estimations, similarity estimations and also unidimensional ratings of intensity. Both individual data and group data were collected. Five different previously suggested content models of similarity failed to fit these data that showed large individual idiosyncratic variability. It is pointed out that previous work, where some of the models fitted rather well, was concerned with cases that were largely insensitive to deviations fram the models.     

Cluster randomized trials with treatment noncompliance

Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed.     

I’m Not Feeling It: Momentary Experiential Avoidance and Social Anxiety Among Individuals With Social Anxiety Disorder

Previous studies have found that social anxiety and experiential avoidance (EA) are significantly associated, but the directionality of this relationship has not been firmly established. The present study examined momentary EA and social anxiety using repeated measurements during an opposite-sex interaction. Participants were 164 individuals (50% female): 42 were diagnosed with social anxiety disorder (SAD) and the remaining 122 were non-socially-anxious individuals (NSAs). Participants formed 42 experimental dyads including 1 individual with SAD and 1 NSA individual, and 40 control dyads including 2 NSA individuals. Lower-level mediational modeling indicated that for individuals with SAD, a reciprocal relationship was observed in which changes in both EA and social anxiety mediated changes in each other. However, changes in EA explained approximately 89% of changes in social anxiety whereas changes in social anxiety explained approximately 52% of changes in EA throughout the interaction. For NSA individuals, only social anxiety predicted EA. These findings point to a deleterious cycle driven mostly by EA among individuals with SAD, but not NSA individuals. Findings are discussed within the context of previous empirical findings as well as acceptance and commitment therapy (ACT) and cognitive-behavioral models of psychopathology.     

The team‐level model of climate for innovation: A two‐level confirmatory factor analysis

The level structure of West's (1990) four‐factor model of team climate for innovation was assessed by means of multi‐level confirmatory factor analysis (MCFA). The sample consisted of 1,487 individuals (195 teams) from a wide range of professions. Results showed that a considerable portion of the variance in the data was explained on the team level with intra‐class correlations ranging from .30 to .39. Furthermore, the results demonstrated that the overall measurement model fitted the data well at both the team and individual levels, while the factor loadings were slightly different across the levels with item loadings showing partial invariance. Results from confirmatory factor analyses conducted on separate levels, however, showed that the four‐factor model displayed the best fit to the data for both individual and team levels. A second‐order one‐factor model also fitted the data well on both levels. The results indicate that the team climate for innovation model can be used as a team‐level consensus model of team climate for innovation.     

Computational modeling of organisational learning by self-modeling networks

Within organisational learning literature, mental models are considered a vehicle for both individual learning and organizational learning. By learning individual mental models (and making them explicit), a basis for formation of shared mental models for the level of the organization is created, which after its formation can then be adopted by individuals. This provides mechanisms for organizational learning. These mechanisms have been used as a basis for an adaptive computational network model. The model is illustrated by a not too complex but realistic case study.     

Les apports de la psychologie sociale aux situations de formation par alternance

This article aims to show how the alternating trainings have been treated by researchers using social psychology approaches. It identifies the theoretical models used, the main researches and some of their results. It comes to the idea that psychology appears as an essential discipline to understand the evolutions of the individuals in these work and training situations but it not sufficiently used. Then, some topics for future researches are designed. The alternating training appears as a very rich situation which allows to understand the individual as well as organizational transformations and the different levels of interrelations of the individual with his/her environment.     

The wisdom of the crowd in combinatorial problems

The "wisdom of the crowd" phenomenon refers to the finding that the aggregate of a set of proposed solutions from a group of individuals performs better than the majority of individual solutions. Most often, wisdom of the crowd effects have been investigated for problems that require single numerical estimates. We investigate whether the effect can also be observed for problems where the answer requires the coordination of multiple pieces of information. We focus on combinatorial problems such as the planar Euclidean traveling salesperson problem, minimum spanning tree problem, and a spanning tree memory task. We develop aggregation methods that combine common solution fragments into a global solution and demonstrate that these aggregate solutions outperform the majority of individual solutions. These case studies suggest that the wisdom of the crowd phenomenon might be broadly applicable to problem-solving and decision-making situations that go beyond the estimation of single numbers.     

An Idiographic Approach to Estimating Models of Dyadic Interactions with Differential Equations

We present an idiographic approach to modeling dyadic interactions using differential equations. Using data representing daily affect ratings from romantic relationships, we examined several models conceptualizing different types of dyadic interactions. We fitted each model to each of the dyads and the resulting AICc values were used to classify the most likely configuration of interaction for each dyad. Additionally, the AICc from the different models were used in parameter averaging across models. Averaged parameters were used in models involving predictors of relationship dynamics, as indexed by these parameters, as well as models wherein the parameters predicted distal outcomes of the dyads such as relationship satisfaction and status. Results indicated that, within our sample, the most likely interaction style was that of independence, without evidence of emotional interrelations between the two individuals in the couple. Attachment-related avoidance and anxiety showed significant relations with model parameters, such that ideal levels of affect for males were negatively influenced by higher levels of avoidance from their partner while their own levels of anxiety had positive effects on their levels of dyadic coregulation. For females coregulation was negatively influenced by both time in the relationship and their partner’s level of avoidance. Analysis involving distal outcomes showed modest influences from the individual’s level of ideal affect.     

Cognitive models of risky choice: parameter stability and predictive accuracy of prospect theory

In the behavioral sciences, a popular approach to describe and predict behavior is cognitive modeling with adjustable parameters (i.e., which can be fitted to data). Modeling with adjustable parameters allows, among other things, measuring differences between people. At the same time, parameter estimation also bears the risk of overfitting. Are individual differences as measured by model parameters stable enough to improve the ability to predict behavior as compared to modeling without adjustable parameters? We examined this issue in cumulative prospect theory (CPT), arguably the most widely used framework to model decisions under risk. Specifically, we examined (a) the temporal stability of CPT’s parameters; and (b) how well different implementations of CPT, varying in the number of adjustable parameters, predict individual choice relative to models with no adjustable parameters (such as CPT with fixed parameters, expected value theory, and various heuristics). We presented participants with risky choice problems and fitted CPT to each individual’s choices in two separate sessions (which were 1 week apart). All parameters were correlated across time, in particular when using a simple implementation of CPT. CPT allowing for individual variability in parameter values predicted individual choice better than CPT with fixed parameters, expected value theory, and the heuristics. CPT’s parameters thus seem to pick up stable individual differences that need to be considered when predicting risky choice.     

Dynamic search and working memory in social recall

What are the mechanisms underlying search in social memory (e.g., remembering the people one knows)? Do the search mechanisms involve dynamic local-to-global transitions similar to semantic search, and are these transitions governed by the general control of attention, associated with working memory span? To find out, we asked participants to recall individuals from their personal social networks and measured each participant's working memory capacity. Additionally, participants provided social-category and contact-frequency information about the recalled individuals as well as information about the social proximity among the recalled individuals. On the basis of these data, we tested various computational models of memory search regarding their ability to account for the patterns in which participants recalled from social memory. Although recall patterns showed clustering based on social categories, models assuming dynamic transitions between representations cued by social proximity and frequency information predicted participants' recall patterns best-no additional explanatory power was gained from social-category information. Moreover, individual differences in the time between transitions were positively correlated with differences in working memory capacity. These results highlight the role of social proximity in structuring social memory and elucidate the role of working memory for maintaining search criteria during search within that structure.     

Computational approaches to motor control

This review will focus on four areas of motor control which have recently been enriched both by neural network and control system models: motor planning, motor prediction, state estimation and motor learning. We will review the computational foundations of each of these concepts and present specific models which have been tested by psychophysical experiments. We will cover the topics of optimal control for motor planning, forward models for motor prediction, observer models of state estimation arid modular decomposition in motor learning. The aim of this review is to demonstrate how computational approaches, as well as proposing specific models, provide a theoretical framework to formalize the issues in motor control.     

Estimating latent distributions in recurrent choice data

This paper introduces a flexible class of stochastic mixture models for the analysis and interpretation of individual differences in recurrent choice and other types of count data. These choice models are derived by specifying elements of the choice process at the individual level. Probability distributions are introduced to describe variations in the choice process among individuals and to obtain a representation of the aggregate choice behavior. Due to the explicit consideration of random effect sources, the choice models are parsimonious and readily interpretable. An easy to implement EM algorithm is presented for parameter estimation. Two applications illustrate the proposed approach.     

People with more misconceptions about automated vehicles might be more positive toward them

Automated vehicle (AV) technology is likely to influence transportation, mobility, and society dramatically. The year 2020 was a horizon year for the AV, as manufacturers expected commercial AVs to be available to the general market. However, we experienced one cycle of hyperbole for these “self-driving” cars, which are still unavailable to consumers. Meanwhile, many persistent beliefs about this technology are factual or arguable misconceptions. However, the public attitude literature rarely examines public misconceptions of AVs. Thus, we explored the prevalence of three misconceptions: “AVs are already available in the market,” “AVs do not need to be driven manually at all,” and “Mature business models for AVs have been established.” We investigated these misconceptions’ correlations with several cognitive (i.e., benefit and risk perceptions), affective (i.e., positive and negative affect), and behavioral components (i.e., behavioral intention and willingness to pay) of attitudes and trust in AVs. Our online survey in China (N = 1,026) indicated that more than 70% of participants hold one or more of the three misconceptions, with one-third believing that AVs are already available in the market. Furthermore, participants believing one or more of the three misconceptions were more positive regarding specific attitudinal factors (e.g., those who believed that AVs are already available reported greater behavioral intention to use and willingness to pay for AVs than those who rejected this misbelief). This finding indicates that people who are more wrong about AVs might be more positive toward AVs. We need effective and accurate public communication to dispel public misconceptions about AVs and build rational expectations.     

Vitalism,purpose and superstition

Developmental studies have shown that children assign purpose to objects more liberally than adults, and that they explain biological processes in terms of vitalistic causality. This study tested the hypothesis that similar misconceptions can be found among superstitious adults. The results from 116 superstitious and 123 sceptical individuals showed that more than sceptics, superstitious individuals attributed purpose to objects, and explained biological processes in terms of organ intentionality and energy transmission. In addition, they thought of energy as a vital force, attributing life and mental properties to it. These conceptual confusions were positively associated to all types of superstitions as well as belief in alternative medicine. The results support the argument that category mistakes and ontological confusions underlie superstitious and vitalistic thinking.     

Bayesian Estimation of Random Coefficient Dynamic Factor Models

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across units. The aims of this study are (a) to propose a random coefficient DFM (RC-DFM) to statistically model variations of dynamics across multiple units using the Bayesian method, (b) to illustrate the use of the proposed procedure by applying RC-DFMs to affect data collected from multiple dyads in romantic relationships, and (c) to evaluate the performance of the RC-DFMs with Bayesian estimation through simulation analyses. The results from the simulation analyses show that the Bayesian estimation of RC-DFMs works well in recovering parameters including both fixed and random effects. A number of practical considerations are provided to guide future research on using Bayesian methods for estimating multivariate time series from multiple units.     

Stability of individual loudness functions obtained by magnitude estimation and production

A correlational analysis of individual magnitude estimation and production exponents at the same frequency was perfor.med, as well as an analysis of individual exponents produced in different sessions by the same procedure across frequency(250, 1, 000, and 3, 000 Hz). Taken together, results show, first, that individual exponent differences do not decrease by counterbalancing magnitude estimation with magnitude production, and, second, that individual exponent differences remain stable over time despite changes in stimulus frequency. Further results disclose that although individual magnitude estimation and production exponents do not necessarily obey the .6 power law, it is possible to predict the slope (exponent) of an equal-sensation function averaged for a group of listeners from individual magnitude estimation and production data. Assuming that individual listeners with sensorineural hearing loss also produce stable and reliable magnitude functions, it is also shown that the slope of the loudness-recruitment function measured by magnitude estimation and production can be predicted for individuals with bilateral losses of long duration. Thus, results obtained in normal and in pathological ears suggest that individual listeners can produce loudness judgments that reveal, albeit indirectly, the input-output characteristic of the auditory system.     

迫选式人格测验的传统计分与IRT计分模型

迫选测验的传统计分方式会产生自模式数据, 不能进行传统的信效度检验、因素分析和方差分析等。近年来研究者提出了一些基于项目反应理论的计分模型, 如瑟斯顿IRT模型和MUPP模型等, 它们可以规避自模式数据的弊端。瑟斯顿IRT模型方便进行参数估计, 模型定义灵活; 而MUPP模型的拓展性较差, 参数估计的方法有待提高。另一方面, 已有研究者基于MUPP模型开发了一些抗作假的迫选测验, 而瑟斯顿IRT模型距离这种应用还比较远。此外, 两个模型的适用性和有效性都有待更多的实证研究来检验。     

Real-time generation of representations for cognitive models

Computerized models of human cognition and behavior have been used to help researchers refine their theories of cognitive processes. Two difficulties inherent in using these cognitive models are that building representations of to-be-modeled groups or individuals is (1) time-consuming and (2) prone to error when performed manually. The use of computerized tools to build representations for cognitive models could alleviate these problems. Such a set of tools has been developed and applied to the task of modeling individual aviation pilots performing flight maneuvers in real time. A study conducted with 27 pilots confirms the accuracy of the computer-generated representations.