Personality and Subjective Well-Being: Evidence from South Korea

Although the statistically significant relationship between personality traits and subjective well-being (i.e., self-reported happiness and life satisfaction) is well-known in the field of positive psychology, some scholars still cast doubt on the external validity of this finding and the strength of personality dimensions vis-à-vis other individual-level determinants of subjective well-being such as income, employment status, marital status, self-reported health, and so on. Using a nationally representative, face-to-face survey fielded in South Korea in 2009, we find that personality traits (measured by the Five-factor Model)—particularly, Emotional Stability and Extraversion—are positively associated with happiness and life satisfaction, after controlling for other covariates. The effects of personality traits are often on par with, and sometimes even greater than, those of other well-known determinants.     

Feminist Discourse and the Hegemonic Role of Mass Media

There has been a notable cultural trend in which feminist concerns are conveyed through many popular culture texts in South Korea since the early 1990s. Many different social groups and organizations have been engaged in the formation of feminist discourse in popular culture, among them the mainstream media. To broadly address the role of media in incorporating feminist discourse within the dominant ideology in specific socio-economic contexts, the research sought to identify the ways in which feminist discourse was generated and/or assimilated into the dominant ideology in newspaper content about the messages in the two television dramas Lovers (1996) and The Woman Next Door (2003) and other socio-cultural phenomena surrounding the dramas. Newspaper content became more favorable to the sexually liberated female characters and acknowledged changing gender roles as a current socio-cultural trend. However, it never questioned the nuclear family system itself—which occupies the hegemonic realm in patriarchal capitalist society.     

Static Numbers to Dynamic Statistics: Designing a Policy-Friendly Social Policy Indicator Framework

In line with the economic crisis and rapid socio-demographic changes, the interest in ??social?? and ??well-being?? indicators has been revived. Social indicator movements of the 1960s resulted in the establishment of social indicator statistical frameworks; that legacy has remained intact in many national governments and international organisations. With this background, this research examines whether existing social indicator frameworks are valid and effective enough to address increasingly complex social issues. The authors argue that, despite some improvements, current social indicators fail to provide an effective framework and tool for measuring the progress of social welfare and also for developing or reforming social policy to cope with newly emerging social problems. While proposing a new social indicator framework based on the Organisation for Economic Co-operation and Development??s pressure-state-response (PSR) model, the paper argues that the new framework should be more than displaying static numbers but should use dynamic statistics revealing causes and effects and shedding light on social and policy changes.     

Simultaneous edit-imputation and disclosure limitation for business establishment data

Business establishment microdata typically are required to satisfy agency-specified edit rules, such as balance equations and linear inequalities. Inevitably some establishments' reported data violate the edit rules. Statistical agencies correct faulty values using a process known as edit-imputation. Business establishment data also must be heavily redacted before being shared with the public; indeed, confidentiality concerns lead many agencies not to share establishment microdata as unrestricted access files. When microdata must be heavily redacted, one approach is to create synthetic data, as done in the U.S. Longitudinal Business Database and the German IAB Establishment Panel. This article presents the first implementation of a fully integrated approach to edit-imputation and data synthesis. We illustrate the approach on data from the U.S. Census of Manufactures and present a variety of evaluations of the utility of the synthetic data. The paper also presents assessments of disclosure risks for several intruder attacks. We find that the synthetic data preserve important distributional features from the post-editing confidential microdata, and have low risks for the various attacks.     

Likelihood ratio tests for multivariate normality

This paper presents some powerful omnibus tests for multivariate normality based on the likelihood ratio and the characterizations of the multivariate normal distribution. The power of the proposed tests is studied against various alternatives via Monte Carlo simulations. Simulation studies show our tests compare well with other powerful tests including multivariate versions of the Shapiro–Wilk test and the Anderson–Darling test.     

Noninformative priors for linear combinations of normal means with unequal variances

For normal populations with unequal variances, we develop matching priors and reference priors for a linear combination of the means. Here, we find three second-order matching priors: a highest posterior density (HPD) matching prior, a cumulative distribution function (CDF) matching prior, and a likelihood ratio (LR) matching prior. Furthermore, we show that the reference priors are all first-order matching priors, but that they do not satisfy the second-order matching criterion that establishes the symmetry and the unimodality of the posterior under the developed priors. The results of a simulation indicate that the second-order matching prior outperforms the reference priors in terms of matching the target coverage probabilities, in a frequentist sense. Finally, we compare the Bayesian credible intervals based on the developed priors with the confidence intervals derived from real data.     

Hill estimator of projections of functional data on principal components

Functional principal component scores are commonly used to reduce mathematically infinitely dimensional functional data to finite dimensional vectors. In certain applications, most notably in finance, these scores exhibit tail behaviour consistent with the assumption of regular variation. Knowledge of the index of the regular variation, α, is needed to apply methods of extreme value theory. The most commonly used method of the estimation of α is the Hill estimator. We derive conditions under which the Hill estimator computed from the sample scores is consistent for the tail index of the unobservable population scores.     

Discovering hidden statistical issues through individual-level models in ecological studies

ABSTRACT

In ecological studies, individual inference is made based on results from ecological models. Interpretation of the results requires caution since ecological analysis on group level may not hold in the individual level within the groups, leading to ecological fallacy. Using an ecological regression example for analyzing voting behaviors, we highlight that the explicit use of individual-level models is crucial in understanding the results of ecological studies. In particular, we clarify three relevant statistical issues for each individual-level models: assessment of the uncertainty of parameter estimates obtained from a wrong model, the use of shrinkage estimation method for simultaneous estimation of many parameters, and the necessity of sensitivity analysis rather than adhering to one seemingly most compelling assumption.     

Frequency domain bootstrap for ratio statistics under long-range dependence

A frequency domain bootstrap (FDB) is a common technique to apply Efron’s independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.     

On nonparametric variogram estimation

A critical step for geostatistical prediction is estimation of variogram from the data. One of the popular methods estimating variogram is a smoothed version of classical nonparametric variogram estimator. In this paper we investigate its theoretical and empirical properties to provide useful information for using it. The main results are based on asymptotic theories (i.e., risk and central limit theorem) under nearly infill domain sampling. Simulation is also employed to make our points.