Using auxiliary data for parameter estimation with non-ignorably missing outcomes

We propose a method for estimating parameters in generalized linear models when the outcome variable is missing for some subjects and the missing data mechanism is non-ignorable. We assume throughout that the covariates are fully observed. One possible method for estimating the parameters is maximum likelihood with a non-ignorable missing data model. However, caution must be used when fitting non-ignorable missing data models because certain parameters may be inestimable for some models. Instead of fitting a non-ignorable model, we propose the use of auxiliary information in a likelihood approach to reduce the bias, without having to specify a non-ignorable model. The method is applied to a mental health study.     

Comparing the Explanatory and Predictive Power of Intention-Based Theories of Personal Monetary Donation to Charitable Organizations

The theory of reasoned action (TRA) and the theory of planned behavior (TPB) have been found to have predictive capability in a wide range of personal behaviors. The aim of the study is twofold: firstly, to assess the applicability of the TRA, the TPB, and a newly developed revised version of the TPB in the context of individuals’ monetary donations to charitable organizations; and secondly, to compare the explanatory and predictive power of these three theoretical models. Data relating to intention to give monetary donation, attitudes toward helping others and toward charitable giving, social norms, moral responsibility, and perceived behavioral control were collected in the first phase of the study by means of a self-completion mail questionnaire distributed to 432 residents of Riyadh, Saudi Arabia. In the second phase, 1 month later, telephone interviews were conducted with 221 of the first-phase respondents who had agreed to take part in a follow-up survey of their actual monetary-donating behavior. The findings show that the revised TPB is the best of the three models for predicting individuals’ intention to donate and their future monetary-donation behavior, mainly because moral responsibility is included in the theoretical framework. It thus offers superior explanatory and predictive power.     

Mixed probability models for dry and wet spells

When identifying the best model for representing the behavior of rainfall distribution based on a sequence of dry (wet) days, focus is usually given on the fitted model with the least number of estimated parameters. If the model with lesser number of parameters is found not adequate for describing a particular data distribution, the model with a higher number of parameters is recommended. Based on several probability models developed by previous researchers in this field, we propose five types of mixed probability models as the alternative to describe the distribution of dry (wet) spells for daily rainfall events. The mixed probability models comprise of the combination of log series distribution with three other types of models, which are Poisson distribution (MLPD), truncated Poisson distribution (MLTPD), and geometric distribution (MLGD). In addition, the combination of the two log series distributions (MLSD) and the mixed geometric with the truncated Poisson distribution (MGTPD) are also introduced as the alternative models. Daily rainfall data from 14 selected rainfall stations in Peninsular Malaysia for the periods of 1975 to 2004 were used in this present study. When selecting the best probability model to describe the observed distribution of dry (wet) spells, the Akaike’s Information Criterion (AIC) was considered. The results revealed that MLGD was the best probability model to represent the distribution of dry spells over the Peninsular.     

Hofstede's cultural dimensions in the GCC countries: An empirical investigation

Managerial values are not the same across the world. Therefore, Multinational Corporations may be making costly decisions if they assume that corporate policies will receive the same attention, understanding or acceptance in their branches over the world. This paper applies Hofstede's four cultural dimensions to the Gulf Cooperation Council (GCC) countries. It also compares Hofstede's results with regard to these four cultural dimensions with our findings. The results of this study show that countries change. They reemphasize the concept that ethnocentric management theories have been untenable. This implies the necessity that Multinational Corporations should study the culture of the country they plan to do business with before starting the negotiations process that precedes the operations in that country and also to monitor changes in the life style of these countries over time. When Multinational Corporations become aware of the international workforce management and the differences between different cultures, they will save themselves costly troubles, loss of qualified personnel as well as loosing international customers. Therefore, it is important for Multinational Corporations to utilize management theories available to them through research and experience.The program leading to this article was supported in part by a grant from KFUPM.     

Bayesian Methods for Missing Covariates in Cure Rate Models

We propose methods for Bayesian inference for missing covariate data with a novel class of semi-parametric survival models with a cure fraction. We allow the missing covariates to be either categorical or continuous and specify a parametric distribution for the covariates that is written as a sequence of one dimensional conditional distributions. We assume that the missing covariates are missing at random (MAR) throughout. We propose an informative class of joint prior distributions for the regression coefficients and the parameters arising from the covariate distributions. The proposed class of priors are shown to be useful in recovering information on the missing covariates especially in situations where the missing data fraction is large. Properties of the proposed prior and resulting posterior distributions are examined. Also, model checking techniques are proposed for sensitivity analyses and for checking the goodness of fit of a particular model. Specifically, we extend the Conditional Predictive Ordinate (CPO) statistic to assess goodness of fit in the presence of missing covariate data. Computational techniques using the Gibbs sampler are implemented. A real data set involving a melanoma cancer clinical trial is examined to demonstrate the methodology.     

张恨水小说的话语世界与传统因缘

张恨水小说的话语与传统因缘有着密切联系。在形式上,张恨水改良了传统的章回体小说;在描写手法上,注重人物心理和场景的描写;在题材上,描写了市民的生活;在思想上,体现了五四时期的新思想。总之,张恨水小说的独特魅力在于它既保留了传统小说的精华,又借鉴了西方学的优点。     

A simple method for estimating a regression model for κ between a pair of raters

Agreement studies commonly occur in medical research, for example, in the review of X-rays by radiologists, blood tests by a panel of pathologists and the evaluation of psychopathology by a panel of raters. In these studies, often two observers rate the same subject for some characteristic with a discrete number of levels. The κ-coefficient is a popular measure of agreement between the two raters. The κ-coefficient may depend on covariates, i.e. characteristics of the raters and/or the subjects being rated. Our research was motivated by two agreement problems. The first is a study of agreement between a pastor and a co-ordinator of Christian education on whether they feel that the congregation puts enough emphasis on encouraging members to work for social justice (yes versus no). We wish to model the κ-coefficient as a function of covariates such as political orientation (liberal versus conservative) of the pastor and co-ordinator. The second example is a spousal education study, in which we wish to model the κ-coefficient as a function of covariates such as the highest degree of the father of the wife and the father of the husband. We propose a simple method to estimate the regression model for the κ-coefficient, which consists of two logistic (or multinomial logistic) regressions and one linear regression for binary data. The estimates can be easily obtained in any generalized linear model software program.     

Estimating Benchmark Exposure for Air Particulate Matter Using Latent Class Models

We performed benchmark exposure (BME) calculations for particulate matter when multiple dichotomous outcome variables are involved using latent class modeling techniques and generated separate results for both the extra risk and additional risk. The use of latent class models in this study is advantageous because it combined several outcomes into just two classes (namely, a high‐risk class and a low‐risk class) and compared these two classes to obtain the BME levels. This novel approach addresses a key problem in risk estimation—namely, the multiple comparisons problem, where separate regression models are fitted for each outcome variable and the reference exposure will rely on the results of the best‐fitting model. Because of the complex nature of the estimation process, the bootstrap approach was used to estimate the reference exposure level, thereby reducing uncertainty in the obtained values. The methodology developed in this article was applied to environmental data by identifying unmeasured class membership (e.g., morbidity vs. no morbidity class) among infants in utero using observed characteristics that included low birth weight, preterm birth, and small for gestational age.     

The Dyadic Effects of Controlling Emotional Abuse on Subjective Emotional Experience in Iranian Older Married Couples

This randomised control trial study examines the impact of an emotional‐focused intervention on emotional abuse and subjective emotional experience in Iranian older married couples. In the study, 57 older couples were randomly assigned into control and experimental groups with the latter taking part in an intervention program over 4 weeks, twice a week, with sessions lasting 90 minutes. In the experimental group, significant improvements were found for reducing emotional abuse behaviours and increasing subjective emotional experience. Dyadic analysis revealed that a partner's ability to control their own emotional abuse significantly effects both his/her subjective emotional experience and that of their spouse.     

Governance structures of socially complex knowledge flows: Exchange, entitlement and gifts

The intangible aspects of knowledge transfer in multinational corporations (MNCs) continue to be a crucial area of research in social science research and raise the issue of knowledge governance structures. The purpose of this conceptual note is twofold. Firstly, we believe that there is more than one governance structure for successful knowledge transfers and flows. We provide a typology of three governance structures: “exchange,” “entitlement,” “gifts.” Secondly, most MNCs will need to take advantage of all three or a combination of these three socially complex governance structures.