The Satisfaction with Sex Life Across the Adult Life Span

Three separate issues concerning the relation between age and satisfaction with sex life are addressed in this article. The first issue was concerned with the age generalizability of the factor structure produced by responses to the Satisfaction with Sex Life Scale (SWSLS). The second issue was to examine whether there were differences in the satisfaction with sex life according to certain background characteristics, namely age. Finally, the relationship between scores on the SWSLS with those on other relational constructs was explored. Data collection involved completion of a questionnaire. The sample consisted of 1,144 participants. The mean ages of the sample were 38.99 years in (SD = 16.91); ages ranged from 20 to 80. The data indicated that the factor structure of responses to the SWSLS were highly similar through adult life. Religious involvement, marital status, and love status influenced satisfaction with sex life. Expected correlations with measures of other relationship constructs were found. The strongest predictor of satisfaction with sex live across the adult life span was love satisfaction. Suggestions concerning the use of the SWSLS for research and clinical purposes are offered.     

我国丝绸出口贸易的经济分析

我国丝绸出口贸易的 "显示"比较优势指数呈逐年下降的趋势,但其数值一直保持在17以上,说明我国的丝绸出口与其他出口商品相比仍具有很大的竞争优势;我国丝绸出口的相对价格弹性系数为-0.3825,属于低弹性的商品.降低丝绸出口价格,不但无益于丝绸出口量的增加,反而会引起出口创汇额的明显下降,引起丝绸"出口贫困"现象的产生.稳定我国丝绸出口价格,是我国蚕丝业和丝绸贸易稳定发展的正确选择.     

When Precaution Creates Misunderstandings: The Unintended Effects of Precautionary Information on Perceived Risks,the EMF Case

In the past decade, growing public concern about novel technologies with uncertain potential long‐term impacts on the environment and human health has moved risk policies toward a more precautionary approach. Focusing on mobile telephony, the effects of precautionary information on risk perception were analyzed. A pooled multinational experimental study based on a 5 × 2 × 2 factorial design was conducted in nine countries. The first factor refers to whether or not information on different types of precautionary measures was present, the second factor to the framing of the precautionary information, and the third factor to the order in which cell phones and base stations were rated by the study participants. The data analysis on the country level indicates different effects. The main hypothesis that informing about precautionary measures results in increased risk perceptions found only partial support in the data. The effects are weaker, both in terms of the effect size and the frequency of significant effects, across the various precautionary information formats used in the experiment. Nevertheless, our findings do not support the assumption that informing people about implemented precautionary measures will decrease public concerns.     

论我国货币政策传导机制中的障碍

通过对近年来我国出现的通货紧缩现象的分析 ,可以看到中央银行所采取的稳健的货币政策的内容及其执行状况。经济的不景气不是由于货币供应量不足而是在于货币政策的传导机制的不畅。我们应找出传导机制不畅的原因 ,并提出相应的对策     

The Age of Confusion: Why So Many Teens are Getting Pregnant,Turning to Welfare and Ending Up Homeless

The ongoing debate over the flaws in the nation's welfare system centers primarily on two highly charged social ills: teen-age pregnancy and long-term dependence on public assistance. For the poorest and fastest growing segment of the welfare population—homeless mothers—these problems are both severe and inextricably linked. At an alarmingly young age, these women are becoming trapped in a chaotic cycle that offers little structure and few alternatives.     

On sufficient conditions for the strong consistency of least-squares estimates

The strong consistency of the least-squares estimates in regression models is obtained when the errors are i.i.d. with absolute moment of order r, 0<r? 2. The assumptions presented for the random error sequence will permit us to obtain improvements of the conditions on the regressors in order to obtain the strong consistency of the least-squares estimates in linear and nonlinear regression models.     

Strong consistency of least squares estimates with i.i.d. errors with mean values not necessarily defined

We establish strong consistency of the least squares estimates in multiple regression models discarding the usual assumption of the errors having null mean value. Thus, we required them to be i.i.d. with absolute moment of order r, 0<r<2, and null mean value when r>1. Only moderately restrictive conditions are imposed on the model matrix. In our treatment, we use an extension of the Marcinkiewicz–Zygmund strong law to overcome the errors mean value not being defined. In this way, we get a unified treatment for the case of i.i.d. errors extending the results of some previous papers.     

New two-stage sampling designs based on neoteric ranked set sampling

Neoteric ranked set sampling (NRSS) is a recently developed sampling plan, derived from the well-known ranked set sampling (RSS) scheme. It has already been proved that NRSS provides more efficient estimators for population mean and variance compared to RSS and other sampling designs based on ranked sets. In this work, we propose and evaluate the performance of some two-stage sampling designs based on NRSS. Five different sampling schemes are proposed. Through an extensive Monte Carlo simulation study, we verified that all proposed sampling designs outperform RSS, NRSS, and the original double RSS design, producing estimators for the population mean with a lower mean square error. Furthermore, as with NRSS, two-stage NRSS estimators present some bias for asymmetric distributions. We complement the study with a discussion on the relative performance of the proposed estimators. Moreover, an additional simulation based on data of the diameter and height of pine trees is presented.     

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.