Published papers

We examined age-related differences in facial expression recognition in association with potentially interfering variables such as general cognitive ability (verbal and visuospatial abilities), face recognition ability, and the experiences of positive and negative emotions. Participants comprised 34 older (aged 62-81 years) and 34 younger (aged 18-25 years) healthy Japanese adults. The results showed not only age-related decline in sadness recognition but also age-related improvement in disgust recognition. Among other variables, visuospatial ability was moderately related to facial expression recognition in general, and the experience of negative emotions was related to sadness recognition. Consequently, age-related decline in sadness recognition was statistically explained by age-related decrease in the experience of negative emotions. On the other hand, age-related improvement in disgust recognition was not explained by the interfering variables, and it reflected a higher tendency in the younger participants to mistake disgust for anger. Possible mechanisms are discussed in terms of neurobiological and socio-environmental factors. © 2006 Elsevier B.V. All rights reserved.

We showed that the propensity score weighted M-estimation method proposed by Hoshino (2005) can be applied in order to adjust for the bias in sampling surveys for marketing research. Further, using real data sets, we investigated the capabilities and the limitations of the propensity score adjustment method to the adjustment of the biased internet surveys and demonstrated the validity of Hoshino's method. By using the covariates selected the method proposed by Hoshino and Maeda (2006) in two similar surveys, we found that the effectiveness of the adjustment method using the propensity score...

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a doubly robust-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods. © 2007 Psychometric Society.

Model selection is one of the most important steps in the application of structural equation modeling (SEM). In this process, the likelihood ratio test statistic, T, is a commonly employed index. Hypothesis testing can be performed on the basis that T asymptotically follows the χ2 distribution. Various fit indices have been proposed, most of which are based on the assumption that T asymptotically follows the χ2 distribution. When the size of the sample is small, however, the distribution of T deviates considerably from the χ2 distribution. This problem, especially pronounced when there is a large number of indicators per factor, is serious because it violates the theoretical justification of utilizing for model selection not only T, but also many other fit indices. In the present article, we propose a Bartlett correction of T to improve its approximation to the χ2 distribution. When the efficacy of our method was evaluated by Monte Carlo simulation, the results showed that this method was superior to the current standard.

In the behavioral and social sciences, quasi-experimental and observational studies are used due to the difficulty achieving a random assignment. However, the estimation of differences between groups in observational studies frequently suffers from bias due to differences in the distributions of covariates. To estimate average treatment effects when the treatment variable is binary, Rosenbaum and Rubin (1983a) proposed adjustment methods for pretreatment variables using the propensity score. However, these studies were interested only in estimating the average causal effect and/or marginal means. In the behavioral and social sciences, a general estimation method is required to estimate parameters in multiple group structural equation modeling where the differences of covariates are adjusted. We show that a Horvitz-Thompson-type estimator, propensity score weighted M estimator (PWME) is consistent, even when we use estimated propensity scores, and the asymptotic variance of the PWME is shown to be less than that with true propensity scores. Furthermore, we show that the asymptotic distribution of the propensity score weighted statistic under a null hypothesis is a weighted sum of independent χ12 variables. We show the method can compare latent variable means with covariates adjusted using propensity scores, which was not feasible by previous methods. We also apply the proposed method for correlated longitudinal binary responses with informative dropout using data from the Longitudinal Study of Aging (LSOA). The results of a simulation study indicate that the proposed estimation method is more robust than the maximum likelihood (ML) estimation method, in that PWME does not require the knowledge of the relationships among dependent variables and covariates. © 2007 The Psychometric Society.

The assessment of individual differences in facial expression recognition is normally required to address two major issues: (1) high agreement level (ceiling effect) and (2) differential difficulty levels across emotions. We propose a new assessment method designed to quantify individual differences in the recognition of the six basic emotions, 'sensitivities to basic emotions in faces.' We attempted to address the two major assessment issues by using morphing techniques and item response theory (IRT). We used morphing to create intermediate, mixed facial expression stimuli with various levels of recognition difficulty. Applying IRT enabled us to estimate the individual latent trait levels underlying the recognition of respective emotions (sensitivity scores), unbiased by stimulus properties that constitute difficulty. In a series of two experiments we demonstrated that the sensitivity scores successfully addressed the two major assessment issues and their concomitant individual variability. Intriguingly, correlational analyses of the sensitivity scores to different emotions produced orthogonality between happy and non-happy emotion recognition. To our knowledge, this is the first report of the independence of happiness recognition, unaffected by stimulus difficulty. © 2005 Elsevier B.V. All rights reserved.

There is contradictory evidence regarding whether the impairments of the recognition of emotional facial expressions in Parkinson's disease are specific to certain emotions such as disgust and fear. Generally, neurological case reports on emotion-specific impairments have been suspected of being confounded with the factor of task difficulty. Using a refined assessment method in which the difficulty factors were controlled by means of mixed facial expressions and item response theory, we attempted to clarify whether Parkinson's disease disproportionately impaired the recognition of specific emotions. We studied 14 patients with Parkinson's disease and 39 healthy controls who were matched in terms of gender, age, years of education and intelligence quotient. Whereas the refined method revealed that the patients with Parkinson's disease displayed significantly lower scores in disgust recognition alone, conventional methods failed to detect this impairment. In addition, control measures including face recognition abilities did not statistically explain the impairment observed in the patients. The results indicate that Parkinson's disease can indeed selectively impair the recognition of facial expressions of disgust; this provides concrete evidence for emotion-specific impairments that sufficiently withstands criticisms regarding the difficulty artefacts. Furthermore, the results support the proposed role of the basal ganglia-insula system in disgust recognition. This study effectively demonstrates the benefits of refining neuropsychological assessment by taking advantage of the modern psychometric theory. © The Author (2006). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.

Psychological research often deals with psychological constructs that cannot be directly measured. Thus independent variables of regression analysis for an observable dependent variable are sometimes latent variables (factors) that are defined independently of the dependent variable. In this study we pointed out the problem associated with the use of factor analysis for the combined set of dependent variable and independent variables in such a cases; that is, the derived factors are different from those originally intended, and the true regression parameters cannot be reproduced. We proposed a stagewise estimation method to solve the problem. This method estimates parameters of measurement equation in the first stage, and then estimates parameters of structural equation in the second stage. Our proposed method enables calculation of standard errors of estimators using Bootstrapping method. Numerical studies showed that the proposed method improves the estimation efficiency over the conventional methods, and provides estimates which are robust with respect to misspecification of model.

The pathways between parenting behaviours, personality and physical health have all been separately studied. Prior research has paid little attention to the indirect effects of personality in the path between parenting behaviours and better health. The purpose of this study was to explore the mediational effects of conscientiousness on the relationships between parental socialisation of responsibility and self-rated health, and to examine potential age differences in this mediational pathway. In total, 736 female and 749 male members across Japan participated in this study. They were divided into three groups by age category: younger-, middle-aged and older-aged. Conscientiousness and health were concurrently rated, while parental socialisation of responsibility was retrospectively assessed. Our analyses revealed that parental socialisation of responsibility is positively associated with conscientiousness and self-rated health, that conscientiousness is positively associated with self-rated health, and that conscientiousness fully mediated the effect of parental socialisation of responsibility on self-rated health. The mediational links were consistent across younger, middle-aged and older-aged cohorts. Our findings suggest that greater parental socialisation of responsibility relates to higher conscientiousness, and consequently healthier adults. These findings imply that parental behaviours could be a plausible target for intervention to foster the development of conscientiousness and better health. © 2012 Copyright Taylor and Francis Group, LLC.

The t-test and the analysis of variance are commonly used as statistical significance testing methods. However, they cannot assess the significance of differences between thresholds within individual observers estimated from the constant stimuli method; these thresholds are not defined as averages of samples, but they are rather defined as functions of parameters of psychometric functions fitted to participants' responses. Moreover, the statistics necessary for these statistical testing methods cannot be derived. In this paper, we propose a new statistical testing method to assess the statistical significance of differences between thresholds estimated from the constant stimuli method. The new method can assess not only threshold differences but also main effects and interactions in multifactor experiments, exploiting the asymptotic normality of maximum likelihood estimators and the characteristics of multivariate normal distributions. This proposed method could be used in similar cases to the analysis of variance for thresholds estimated from the adjustment method and the staircase method. Finally, we present some data on simulations in which we tested assumptions, power and type I error of the proposed method. © 2011 BRILL.