The paper examines democratic system support in Japan during the recent period of economic decline. It confronts the notion that contemporary Japanese political legitimacy is based primarily upon its economic achievements by asking whether a prolonged economic crisis has moved its citizens to embrace authoritarian forms of government. Data from two European surveys is used to compare the “default dimension” in Japanese democratic system support with other industrial democracies. While identifying a comparatively large degree of system apathy in Japan, I argue that the lack of support for alternative forms of government indicates democracy in Japan remains consolidated. In distinguishing the role that negative orientations toward the war-era government play in anchoring a participatory political culture I hope to create a better understanding of the particular characteristics of Japanese political legitimacy.

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In the present paper, the ordinal Play-the-Winner sampling with inverse stopping rules is generalized in view of the number of treatments, the introduction of an available sample size and so on. The probability of the correct and wrong selection and the expected number of total sample size are formulated by using the negative binomial distribution without any use of the asymptotic theory. Monte Carlo experiments are performed for some practical situations to look into several properties numerically. As a criterion for optimality of such plans, the minimization of the expected loss proposed by Colton is used with several probabilities and expectation of random numbers to design an optimal Generalized Play-the-Winner plan.

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The aim of this paper is to apply Bayesian methods via the Gibbs sampler to multivariate normal mixtures whose means and covariance matrices are structured as confirmatory factor analysis models. This estimation method uses the Gibbs sampling, and does not rely on the asymptotic theory nor on any other “sophisticated” MCMC methods. And yet, it can handle easily the cases where common parameterization between components is assumed and/or some parameters are linearly constrained (e.g., they are equal), which was impossible in previous studies. A simulation study showed that the proposed method is effective even for data in which the degree of separation is so small that the asymptotic theory could not apply. It is also shown that the proposed method applied to real data produced results capable of meaningful interpretation.

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This paper starts with a general introduction into measurement of hypothetical constructs typical of the social and behavioral sciences. After the stages ranging from theory through operationalization and item domain to preliminary test or questionnaire have been treated, the general assumptions of item response theory are discussed. The family of parametric item response models for dichotomous items (e.g., correct/incorrect scores) is introduced and it is explained how parameters for respondents and items are estimated from the scores collected from a sample of respondents who took the test or questionnaire. Next, the family of nonparametric item response models is explained, followed by the three classes of item response models for polytomous item scores (e.g., rating scale scores). Then, it is discussed to what degree the mean item score (the p-value for dichotomous items) and the unweighted sum of item scores for persons (the total test score) are useful for measuring items and persons in the context of item response theory. The concepts of invariant item ordering for items, and monotone likelihood ratio, stochastic ordering, and ordering of the expected latent trait for persons, are relevant here. So far, the paper has concentrated on measurement of properties of persons and items, based on item response models. Such measurements make sense only when the item response model fits the data. Methods for fitting models to data are briefly discussed for parametric and nonparametric models, but also two recent hybrid methods are mentioned. Finally, the main applications of item response models are discussed, which include equating and item banking, computerized and adaptive testing, research into differential item functioning, person fit research, and cognitive modeling.

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