A variable-selection heuristic for K-means clustering

One of the most vexing problems in cluster analysis is the selection and/or weighting of variables in order to include those that truly define cluster structure, while eliminating those that might mask such structure. This paper presents a variable-selection heuristic for nonhierarchical (K-means) cluster analysis based on the adjusted Rand index for measuring cluster recovery. The heuristic was subjected to Monte Carlo testing across more than 2200 datasets with known cluster structure. The results indicate the heuristic is extremely effective at eliminating masking variables. A cluster analysis of real-world financial services data revealed that using the variable-selection heuristic prior to the K-means algorithm resulted in greater cluster stability.     

ALTRUISM: TOWARD A PSYCHOBIOSPIRITUAL CONCEPTUALIZATION

Altruism, defined here as a regard for or devotion to the interest of others with whom we are interrelated, is pitted against two other dispositions in human beings: nepotism and egoism. We propose that to become fully human is to become more altruistic. We describe how altruism is mediated by our physiology, is expressed in our psychological development, is evolving in our social institutions, and becomes the moral communities that enforce our sense of right and wrong. A change in any one of these influences changes our disposition—changes who we are and what we do—potentially making altruism more possible in the world.     

An overview of applied robust methods

Data in social sciences are typically non-normally distributed and characterized by heavy tails. However, most widely used methods in social sciences are still based on the analyses of sample means and sample covariances. While these conventional methods continue to be used to address new substantive issues, conclusions reached can be inaccurate or misleading. Although there is no ‘best method’ in practice, robust methods that consider the distribution of the data can perform substantially better than the conventional methods. This article gives an overview of robust procedures, emphasizing a few that have been repeatedly shown to work well for models that are widely used in social and behavioural sciences. Real data examples show how to use the robust methods for latent variable models and for moderated mediation analysis when a regression model contains categorical covariates and product terms. Results and logical analyses indicate that robust methods yield more efficient parameter estimates, more reliable model evaluation, more reliable model/data diagnostics, and more trustworthy conclusions when conducting replication studies. R and SAS programs are provided for routine applications of the recommended robust method.     

A bayesian estimation of factor score in confirmatory factor model with polytomous, censored or truncated data

The posterior analysis in estimating factor score in a confirmatory factor analysis model with polytomous, censored or truncated data is investigated in this paper. For the above three types of data, posterior distributions of the factor score are studied, and the estimators of the factor score are obtained to be the location parameters of the posterior distributions. The accuracy of Bayesian estimates is studied via simulation studies.This research was supported by a Hong Kong UGC grant.     

Stimulus-specific contrast effects during operant discrimination learning

In two experiments, pigeons' responding was equally reinforced in the presence of four line-orientation stimuli. Responding was then reinforced when only two of the four orientation stimuli were present; the remaining two orientations appeared during extinction. Response rates were often highest in the stimulus adjacent to the orientations presented during extinction and often lowest in that orientation adjacent to the orientations presented with reinforcement. These effects were stronger and more persistent when the stimuli were separated by a smaller angle, rendering the discrimination more difficult. These and other data suggest that discrimination training may not be accurately explained in terms of the simple effects of reinforcement and nonreinforcement associated with isolated stimuli, nor by accounts that depend upon stimulus generalization. Recent accounts of contrast that depend upon “emotionality” produced by nonreinforced responding or upon reinforcement-elicited responses are also difficult to apply to these data.     

Matching with a trio of concurrent variable-interval schedules of reinforcement

A trio of concurrent variable-interval schedules of reinforcement was arranged according to a changeover-key procedure, including a changeover delay of 1.5 sec. The three schedules provided a combined maximum reinforcement rate of 45 reinforcements per hour. With that restriction, the nine experimental conditions included several combinations of variable-interval schedules, sometimes including extinction. The pigeons matched relative response rate and relative time to relative reinforcement rate. Relative time appeared to match some-what better than relative response rate. Performance adjusted rapidly from one experimental condition to the next, whether the change involved two or all three schedules of the concurrent trio.     

RESPONSE REDUCTION THROUGH THE SUPERIMPOSITION OF CONTINUOUS REINFORCEMENT: A SYSTEMATIC REPLICATION

Prior clinical research suggests that superimposition and subsequent removal of a schedule of continuous reinforcement (CRF) may be a viable rate-decreasing procedure in that an extinction-like condition is arranged. The arrangement of similar conditions in the laboratory, however, resulted in the quick recovery of baseline rates. Lever-pressing patterns of eight male rats maintained by different schedules of variable-ratio and variable-interval food reinforcement were examined in an A-B-A experimental design of CRF food superimposition and removal. Responding was substantially reduced during the superimposition of CRF. Upon removal of the superimposed schedule, responding quickly approached presuperimposition baseline rates.     

Comparing item characteristic curves

Test items are often evaluated and compared by contrasting the shapes of their item characteristics curves (ICC's) or surfaces. The current paper develops and applies three general (i.e., nonparametric) comparisons of the shapes of two item characteristic surfaces: (i) proportional latent odds, (ii) uniform relative difficulty, and (iii) item sensitivity. Two items may be compared in these ways while making no assumption about the shapes of item characteristic surfaces for other items, and no assumption about the dimensionality of the latent variable. Also studied is a method for comparing the relative shapes of two item characteristic curves in two examinee populations.The author is grateful to Paul Holland, Robert Mislevy, Tue Tjur, Rebecca Zwick, the editor and reviewers for valuable comments on the subject of this paper, to Mari A. Pearlman for advice on the pairing of items in the examples, and to Dorothy Thayer for assistance with computing.     

A comparison of response patterns on fixed-, variable-, and random-ratio schedules

The behavior of individual pigeons on fixed-ratio, variable-ratio, and random-ratio schedules was examined. Within each type of ratio schedule the size of the ratio was varied in an irregular sequence. At various ratio sizes (5, 10, 40, 80) no differences were found among overall response rates (postreinforcement pause plus running response rate) as a function of ratio type. This similarity in overall response rates held despite noticeable differences in the microstructure of performance both within and across subjects; the primary performance difference on the three types of ratio schedules was the relatively longer postreinforcement pause duration on the fixed-ratio schedule. We concluded that the gross temporal characteristics of performance determined by the relative weightings of the postreinforcement pause and running response rate were primarily controlled by the type of ratio schedule (fixed, variable, or random), whereas the overall rate of responding was controlled by the size of the ratio.