The first comparative survey in contemporary China (n=2, 000) and Japan (n=483) found vastly different patterns of cultural values. In highly modernized Japan, most traditional cultural values have remained intact. In China, after thirty years of class struggle including the Cultural Revolution, many of the same traditional values were cast in doubt or rejected. Even those traditional values that were still upheld in a limited way did not form a core, but were fragmented. In Japan, in addition to the traditional majority, we found a parallel group of counter values that deviated from traditionalism. Those deviants seemed to experience social conflicts and psychological tension in the process of cultural change. In China, the trend was the opposite. The minority of Chinese still following traditional values experienced more social conflicts and psychological tension.
A structural model of the ability distribution in the item response theory is proposed in which continuous covariates representing the properties of groups to which the subjects belong are involved. The ability parameter which is treated as a random variable, is regressed on the covariates. It is assumed that the ability parameter is normally distributed with the mean and variance which are functions of the covariates. A two-parameter logistic model is adopted as the probability model of an item response. It is shown that item parameters and hyperparameters in the ability distribution can be estimated simultaneously using the EM algorithm. Examples are provided, where the mean and standard deviation of the ability parameter are represented by polynomials of age for data of an adult intelligence test.
This paper presents statistical procedures for classifying records of psychophysiological responses obtained in the guilty knowledge polygraph tests. The procedures to be presented are divided into a within-subject approach and a training-data approach. In the former approach, the classification is made using only the record to be classified, whereas in the latter the rule for the classification is derived using training-data. The procedures were evaluated with records obtained in an experiment. The major result was that both the approaches yielded almost 80% correct classifications for the records containing a sufficient number of observations, but the within-subject approach became inaccurate for the records with few observations in contrast to the training-data approach showing stable outcomes. It was also shown that the statistical procedures were superior to the diagnostic procedure relying on human interpretations of the records.
The feed-forward neural network model can be considered a very poweful nonlinear regression analytic tool. However, the existing popular back-propagation algorithm using the steepest descent method is very slow to converge prohibiting the every day use of the neural network regression model. In this regard, a fast converging algorithm for the estimation of the weights in feed-forward neural network models was developed using the alternating least squares (ALS) or conditional Gauss-Newton method. In essence the algorithm alternates the minimization of the residual sums of squares (RSS) with respect to the weights in each layer until the reduction of RSS is negligible. With this approach, neither the calculation of a complex second derivative matrix nor the inversion of a large matrix is necessary. In order to avoid the inflation of the weight values, a ridge method and a quasi Bayesian method were also investigated. The methods were evaluated using several problems and found to be very fast compared to the steepest descent method. With a fast converging algorithm at hand, it is hoped that the statistical nature of the neural network model as a nonlinear regression analysis model is clearly revealed.
For square contingency tables with ordered categories, two kinds of measures are proposed to represent the degree of departure from global symetry (GS), which means that the probability that an observation will fall in one of cells in the upper right triangle of square table is equal to the probability that the observation falls in one of cells in the lower left triangle of it. One measure is expressed by using the Kullback-Leibler information (or Shannon entropy) and the other is expressed by using the Pearson's chisquared type discrepancy (or Gini concentration). These measures would be useful for comparing the degree of departure from GS in several tables.
Though solidly founded by famous sociologists earlier in the twentieth century, comparative sociology lost its foothold and languished in recent decades. However, interest in cross-national studies is rekindling. Unfortunately, cross-national attitudinal surveys are extraordinarily complex and expensive. The many problems of such research are discussed in terms of design, development and execution but not data analysisNote 1). An optimization approach is suggested to achieve a realistic and practical level of comparability.