The Possibility of Mixed-Mode Surveys in Sociological Studies

Abstract:  This paper explores the possibility of the mixed-mode survey in Japan in order to ensure a wider range of respondents, focusing on the specific type of mixture that is designed to make contact with a respondent by more than one mode and in which a respondent can choose an alternative mode convenient for him or her. The dataset mainly used comes from the survey Study on the Social Composition of the Urban Local Community conducted in Shinagawa Ward of Tokyo in 1999. Three other studies are introduced for additional information. As an indicator of the effectiveness of a mixed-mode design, the response rates of four studies are examined and some improvement in the response rates is observed, compared to that of a single-mode design. Another indicator is the representativeness of a sample and it is evaluated using the data of the Shinagawa survey. When the discrepancy between valid respondents and the population on some demographic characteristics is regarded as representation bias, a mixed-mode design is found to reduce the bias. An inherent problem in a mixed-mode design is "mode effect." In order to control for the mode effect in analyses, the inclusion of an additional variable indicating "mode" in the model is recommended. It is further suggested that the interaction effects between the "mode" and the respondents' traits should be checked, on the assumption that the survey mode affects respondents differently. The attempts are made and discussed in this paper using two variables drawn from the Shinagawa survey.     

Risk Assessment of the Carbon Nanotube Group

This study assessed the health risks via inhalation and derived the occupational exposure limit (OEL) for the carbon nanotube (CNT) group rather than individual CNT material. We devised two methods: the integration of the intratracheal instillation (IT) data with the inhalation (IH) data, and the “biaxial approach.” A four‐week IH test and IT test were performed in rats exposed to representative materials to obtain the no observed adverse effect level, based on which the OEL was derived. We used the biaxial approach to conduct a relative toxicity assessment of six types of CNTs. An OEL of 0.03 mg/m³ was selected as the criterion for the CNT group. We proposed that the OEL be limited to 15 years. We adopted adaptive management, in which the values are reviewed whenever new data are obtained. The toxicity level was found to be correlated with the Brunauer‐Emmett‐Teller (BET)‐specific surface area (BET‐SSA) of CNT, suggesting the BET‐SSA to have potential for use in toxicity estimation. We used the published exposure data and measurement results of dustiness tests to compute the risk in relation to particle size at the workplace and showed that controlling micron‐sized respirable particles was of utmost importance. Our genotoxicity studies indicated that CNT did not directly interact with genetic materials. They supported the concept that, even if CNT is genotoxic, it is secondary genotoxicity mediated via a pathway of genotoxic damage resulting from oxidative DNA attack by free radicals generated during CNT‐elicited inflammation. Secondary genotoxicity appears to involve a threshold.     

Marginal asymmetry model for square contingency tables with ordered categories

For the analysis of square contingency tables with ordered categories, this paper proposes a model which indicates the structure of marginal asymmetry. The model states that the absolute values of logarithm of ratio of the cumulative probability that an observation will fall in row category i or below and column category i+1 or above to the corresponding cumulative probability that the observation falls in column category i or below and row category i+1 or above are constant for every i. We deal with the estimation problem for the model parameter and goodness-of-fit tests. Also we discuss the relationships between the model and a measure which represents the degree of departure from marginal homogeneity. Examples are given.     

The generalized Cucconi test statistic for the two-sample problem

When testing hypotheses in two-sample problem, the Lepage test statistic is often used to jointly test the location and scale parameters, and this test statistic has been discussed by many authors over the years. Since two-sample nonparametric testing plays an important role in biometry, the Cucconi test statistic is generalized to the location, scale, and location–scale parameters in two-sample problem. The limiting distribution of the suggested test statistic is derived under the hypotheses. Deriving the exact critical value of the test statistic is difficult when the sample sizes are increased. A gamma approximation is used to evaluate the upper tail probability for the proposed test statistic given finite sample sizes. The asymptotic efficiencies of the proposed test statistic are determined for various distributions. The consistency of the original Cucconi test statistic is shown on the specific cases. Finally, the original Cucconi statistic is discussed in the theory of ties.     

Estimation of the optimal portfolio weights by shrinking the mean vector towards a linear subspace

To obtain estimators of mean-variance optimal portfolio weights, Stein-type estimators of the mean vector that shrink a sample mean towards the grand mean have been applied. However, the dominance of these estimators has not been shown under the loss function used in the estimation problem of the mean-variance optimal portfolio weights, which is different than the quadratic function for the case in which the covariance matrix is unknown. We analytically give the conditions for Stein-type estimators that shrink towards the grand mean, or more generally, towards a linear subspace, to improve upon the classical estimators, which are obtained by simply plugging in sample estimates. We also show the dominance when there are linear constraints on portfolio weights.