Using survey results of Japanese Americans (JA) living on the West Coast of the USA in 1998, and JA and non-JA living in Hawaii in 1999-2000, we examined cultural links between native Japanese, JAs, and Americans. For data on Japanese and Americans, survey results from 1988 were used for the analysis. To understand the social and cultural similarities and differences among Japanese, JAs, and American society groups, we examined interpersonal attitudes, ways of thinking, religious attitudes, and so forth. The results revealed that Japanese-like attitudes were maintained in both society groups of JA. The cultural links of these society groups was not one-dimensional.
This paper analyzes the longitudinal changes of Japanese attitudes toward nature and environment (ATNE) using Japanese character survey data, and explores the structural characteristics of response patterns to the ATNE in Japan, USA and five European nations based on analytical results derived from cross-national survey data. We also discuss the relationships of respondent’s attributes relative to the structure of their response patterns using the visual configuration obtained by correspondence analysis. In addition, we verify how racial, cultural, geographic diversities change people’s ATNE in various circumstances. As a result, we have found that Japan is similar to France, and Germany is similar to Britain and the Netherlands. We have also seen that the USA is similar to Italy in the aspect of the ATNE whereas the Japanese have their own distinctive structures of response patterns to the ATNE when compared to the Americans and the Europeans.
The study represents an attempt to develop a theory of Japanese Americans in comparative perspective in relation to Americans at large and Japanese. I propose that Japanese Americans are the way they are because of their endogenous and exogenous factors: 1) ethno-religious heritage and 2) ethnic composition and social structure in the community. They are basically Americans, language-wise and as far as identity and loyalty are concerned. However, they maintain certain physical as well as ethnic heritages derived from their ancestral traditions. These include such institutions as religion, martial arts, food and a variety of hobbies to fine arts. This is carried on through organizational efforts of the Japanese American community in Hawaii. They constitute a color or two in the rainbow of colors represented in Hawaii. Hence, we propose the rainbow model of Japanese Americans in Hawaii.
The asymptotic correlations among maximum likelihood (ML) and various least squares (LS) estimators in factor analysis are derived. The LS estimators include the unweighted (ULS) and weighted estimators for unstandardized variables and the ULS estimators for standardized variables. The derived formulas cover the cases with restrictions on parameters. Numerical examples with simulations are provided to confirm the accuracy of the formulas and the influence of scales on the asymptotic correlations.
In quantifying a two-way table of data, one derives weights (spacings) for rows and columns so as to maximize the correlation of data weighted by row weights and those by column weights. It is well known that one can calculate the distance between any two rows (columns), called the within-set distance, but the distance between a row and a column, the between-set distance, cannot be calculated because the row weights span the space different from that of the column weights. The present paper shows (1) that one can calculate the between-set distances, and (2) that the data as a whole require an additional dimension to accommodate the discrepancy of the row space and the column space. Since the information contained in the between-set distances is an integral part of the data structure, it is not a matter of whether the study advocates the use of the between-set distance information, but rather it is a problem that dual scaling and correspondence analysis must deal with through development of an analytic method to tap into the entire information in the data. Until then, the current formulation of these methods which ignore the information contained in the between-set distances provide at best simplified approximations to the decomposition of data structure. This further development is not easy because these methods are by the very nature of categorical data based on the chi-square metric, making an entire inter-point (within-set and between-set) distance matrix not readily amenable to any currently available analytic method.
Data are said to be ipsative when the sum of the measures obtained over the variables is a constant for each individual. In this article, three types of ipsative data that are commonly encountered in social research are discussed. Each of them can be used to control the effect of different response set biases. Using an artificial example in the context of factor analysis, however, it is showed that the three types of ipsative data cannot simply be analyzed as if they were normative because this will give us misleading and improper results. Correct methods for factor-analyzing the additive and the ordinal ipsative data are discussed. Empirical results from our examples indicate that the suggested methods recover the normative factor structure successfully. It is concluded that ipsative data, if analyzed appropriately, can provide us useful information in psychological research.