Fragile promise: The role of justification in promise-breaking

People who consider themselves moral sometimes use self-serving justifications to rationalize their selfish behaviours. Previous studies have tested the role of ambiguity in justifying wrongdoings, but it remains unclear whether ambiguity also plays a role in justifying promise-breaking behaviour and whether heterogeneity exists. To investigate justification in promise-breaking, we introduced a new experimental paradigm called the card-guessing task and used hierarchical cluster analysis to classify participants based on their promise-breaking decisions in unambiguous and ambiguous conditions. Experiment 1 revealed three clusters of solutions: Cluster 1 always kept their promises (i.e., keepers); Cluster 2 only exploited the vague promises and broke their promises in the ambiguous condition (i.e., intermediates); Cluster 3 tended to take advantage of vague promises and broke their promises irrespective of ambiguity (i.e., breakers). Experiment 2 confirmed that participants in the three clusters differed in their norm-abiding preferences and social value orientations. Keepers were more altruistic and had a stronger sense of norm compliance than intermediates and breakers. In conclusion, our study demonstrates that self-serving justifications were more likely to be employed by people who are moderately sensitive to deviation from social norms, which has implications for strategic interventions and policy formulation concerning unethical behaviour.     

Validity of the DSM-III-R personality disorder Clusters

This study assessed the validity of the DSM-III-R personality disorder clusters (i.e., odd-eccentric, dramatic-emotional-erratic, and anxious-fearful) by examining the relationships between self-report measures that tap the core features shared by disorders from each cluster and Cluster scores established via a semistructured interview in a sample of 57 outpatients. Results indicated a high degree of correlation among the DSM-III-R personality disorder Cluster scores. In addition, a series of regression analyses revealed that self-report scores did not account for a significant amount of variance in their respective Cluster scores over and above that accounted for by other self-report measures and other Cluster scores. These results suggest that the current DSM-III-R cluster classification scheme may not be appropriate, and it is recommended that a more empirically justifiable classification of the personality disorders be adopted in DSM-IV.     

Comparative evaluation of two superior stopping rules for hierarchical cluster analysis

A split-sample replication stopping rule for hierarchical cluster analysis is compared with the internal criterion previously found superior by Milligan and Cooper (1985) in their comparison of 30 different procedures. The number and extent of overlap of the latent population distributions was systematically varied in the present evaluation of stopping-rule validity. Equal and unequal population base rates were also considered. Both stopping rules correctly identified the actual number of populations when there was essentially no overlap and clusters occupied visually distinct regions of the measurement space. The replication criterion, which is evaluated by clustering of cluster means from preliminary analyses that are accomplished on random partitions of an original data set, was superior as the degree of overlap in population distributions increased. Neither method performed adequately when overlap obliterated visually discernible density nodes.This research was supported in part by NIMH grant 5R01 MH 32457 14.     

How much woe when we go: A quantitative method for predicting culture shock

Culture shock is defined as the confusion and discomfort caused by the conflict in perceived motives and expected behaviors between the home culture and the foreign culture. Several quantitative and graphical methods employing techniques of cluster analysis and similarity mapping are offered for predicting the magnitude of culture shock between pairs of countries using data extracted from Hofstede's 1980–83 studies of national cultural values. Implications for business, politics, and personal stress management are discussed.     

Gennclus: New models for general nonhierarchical clustering analysis

A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These (metric) clustering models generalize the Shepard-Arabie Additive Clusters model in allowing for: (1). either overlapping or nonoverlapping clusters; (2). either symmetric (one-way clustering) or nonsymmetric (two-way clustering) proximities (input data); and, (3). either symmetric or diagonal weights. The GENNCLUS algorithms utilize alternating least-squares methods combining ordinary and constrained least-squares, nonlinear constrained mathematical programming, and combinatorial optimization techniques in estimating model parameters. In addition to developing the mathematical bases of these models, a comprehensive set of Monte Carlo simulations of the different models is reported. Two applications concerning brand-switching data and celebrity-brand proximities are discussed. Finally, extensions to three-way models, nonmetric analyses, and other model specifications are provided.Wayne S. DeSarbo is a Member of Technical Staff in the Mathematics and Statistics Research Center at Bell Laboratories in Murray Hill, N.J. I wish to thank R. Gnanadesikan, J. D. Carroll, and P. Arabie for their comments on a previous draft of this paper. I also wish to acknowledge the computer assistance provided by Linda Clark. Finally, I wish to thank the reviewers and editor for their very complete reviews and comments.     

Ordered binary trees constructed through an application of Kendall's tau

A procedure is described for orienting the nodes of a binary tree to maximize the Kendall rank order correlation (tau) between node order and a given external criterion. The procedure is computationally efficient and is based on application of an ordered set of node tests.     

Effects of training and display content on Level 2 driving automation interface usability

IntroductionLevel 2 driving automation features, such as adaptive cruise control (ACC) combined with lane centering, primarily communicate their operating statuses through the instrument cluster. It remains unclear how interface-specific training and display content influence the ability to understand Level 2 activity in production vehicles.MethodsEighty participants viewed videos recorded from the driver’s point of view in a variety of driving scenarios with Level 2 activity displayed in the instrument cluster of a 2017 Mercedes-Benz E-Class. Half viewed one of two instrument cluster layouts (simple or complex), and half received an orientation to the interface before the experiment. After each video, they were asked about the scenario they had just seen. We then examined what information in the displays participants used to identify Level 2 activity and the usability of the displays.ResultsTraining improved recognition accuracy of when lane centering was temporarily inactive and understanding of why it was inactive. Neither training nor display content affected the ability to recognize when ACC had adjusted the vehicle’s speed or detected a vehicle ahead, or when ACC initially did not detect a lead vehicle and understanding of why it had not detected it. Both factors influenced which sources of information participants used to determine Level 2 activity. Recognition accuracy of system activity improved when participants used valid sources of information. Training, but not display content, improved lane centering usability, but not ACC usability.DiscussionBasic training improves detection of notifications that potentially require further driver action, but not of those that display persistent status information. Training does not result in full understanding of all system notifications or functional limitations, which reinforces the need for intuitive, salient communication about system behavior and its limitations.     

MMPI-2 subgroups in a sample of chronic pain patients

MMPI-2 scores of 307 female and 161 male chronic pain patients were analyzed by gender using a multivariate clustering method. Two subgroups were found for both sexes replicating previous results. The major subgroup corresponded to the classical "Conversion V" and the minor corresponded to the "Generally elevated" profile. The results also indicated a satisfactory internal consistency and a high discriminant validity of the Swedish version of the MMPI-2.     

Getting the Big Picture in Community Science: Methods That Capture Context

Community science has a rich tradition of using theories and research designs that are consistent with its core value of contextualism. However, a survey of empirical articles published in the American Journal of Community Psychology shows that community scientists utilize a narrow range of statistical tools that are not well suited to assess contextual data. Multilevel modeling, geographic information systems (GIS), social network analysis, and cluster analysis are recommended as useful tools to address contextual questions in community science. An argument for increased methodological consilience is presented, where community scientists are encouraged to adopt statistical methodology that is capable of modeling a greater proportion of the data than is typical with traditional methods.     

A cautionary note on using internal cross validation to select the number of clusters

A highly popular method for examining the stability of a data clustering is to split the data into two parts, cluster the observations in Part A, assign the objects in Part B to their nearest centroid in Part A, and then independently cluster the Part B objects. One then examines how close the two partitions are (say, by the Rand measure). Another proposal is to split the data into k parts, and see how their centroids cluster. By means of synthetic data analyses, we demonstrate that these approaches fail to identify the appropriate number of clusters, particularly as sample size becomes large and the variables exhibit higher correlations.The authors express their thanks to the Sol C. Snider Entrepreneurial Center, Wharton School, for support of this project.