Division of Household Labor and Social Judgments in Israel: The Influence of Gender and Education

The study investigated how men and women with high and low levels of education perceive male and female targets who participate or do not participate in household chores. It was found that individuals liked both men and women who participated in the household chores more and wanted to engage in activities with them more than with the low‐participating targets. The participating man was perceived as more popular than the low‐participating man and was perceived as more feminine but not less masculine. In addition, although participants with both high and low levels of education preferred the participating man, the more educated participants preferred him more, attributed more masculinity to him, and expressed willingness to befriend him and engage in activities with him more than those with a lower level of education. It seems, then, that whereas in the 1990s both highly and less educated individuals perceive a male target who participates in household chores more favorably, this preference is more pronounced among the more educated individuals.     

Employee involvement programs: Should unions get involved?

We test the mediated effects of participation in employee involvement (EI) programs on employee desires for union involvement in the future development and diffusion of EI and the perceived durability of an EI program as a possible moderator of these effects. Results indicate that participants developed more positive attitudes toward the EI program than nonparticipants and, in turn, these attitudes were related to higher desires for union involvement in EI. Additionally, volunteers developed more positive attitudes toward the EI program than nonvolunteers and, in turn, these attitudes were related to higher desires for union involvement in EI. However, the relationship between attitudes toward EI and desires for union involvement in EI was significantly less positive for participants who viewed the EI program as durable.     

Influence of human immunodeficiency virus infection on neurological impairment: an analysis of longitudinal binary data with informative drop-out

A study to investigate the human immunodeficiency virus (HIV) status on the course of neurological impairment, conducted by the HIV Center at Columbia University, followed a cohort of HIV positive and negative gay men for 5 years and assessed the presence or absence of neurological impairment every 6 months. Almost half of the subjects dropped out before the end of the study for reasons that might have been related to the missing neurological data. We propose likelihood-based methods for analysing such binary longitudinal data under informative and non-informative drop-out. A transition model is assumed for the binary response, and several models for the drop-out processes are considered which are functions of the response variable (neurological impairment). The likelihood ratio test is used to compare models with informative and non-informative drop-out mechanisms. Using simulations, we investigate the percentage bias and mean-squared error (MSE) of the parameter estimates in the transition model under various assumptions for the drop-out. We find evidence for informative drop-out in the study, and we illustrate that the bias and MSE for the parameters of the transition model are not directly related to the observed drop-out or missing data rates. The effect of HIV status on the neurological impairment is found to be statistically significant under each of the models considered for the drop-out, although the regression coefficient may be biased in certain cases. The presence and relative magnitude of the bias depend on factors such as the probability of drop-out conditional on the presence of neurological impairment and the prevalence of neurological impairment in the population under study.     

Linking the university and the social agency in collaborative evaluation research: principles and examples

This paper presents an analysis of components of successful social work evaluation research practice. Among the key obstacles to successful evaluations of social work intervention are the impoverished knowledge base on which many social work interventions are based, the isolation of researchers and practitioners in the conduct of evaluations, and during research implementation failure to cope with the changing field context. To remedy this situation social work needs to invest in university and social agency partnerships focussed on knowledge building for the profession. In such partnerships the traditional roles of the researcher and the practitioner are altered so that shared responsibility for knowledge development is possible. In addition, to deal with changing field contexts the research team needs to be continuously engaged in problem solving and redesign. An initial example of such a partnership is described together with a case study illustrating implementation of a multi-site field experiment evaluation.     

Mixture structure analysis using the Akaike Information Criterion and the bootstrap

Given i.i.d. observations x1,x2,x3,...,xn drawn from a mixture of normal terms, one is often interested in determining the number of terms in the mixture and their defining parameters. Although the problem of determining the number of terms is intractable under the most general assumptions, there is hope of elucidating the mixture structure given appropriate caveats on the underlying mixture. This paper examines a new approach to this problem based on the use of Akaike Information Criterion (AIC) based pruning of data driven mixture models which are obtained from resampled data sets. Results of the application of this procedure to artificially generated data sets and a real world data set are provided.     

Robust models in probability sampling

In the estimation of a population mean or total from a random sample, certain methods based on linear models are known to be automatically design consistent, regardless of how well the underlying model describes the population. A sufficient condition is identified for this type of robustness to model failure; the condition, which we call 'internal bias calibration', relates to the combination of a model and the method used to fit it. Included among the internally bias-calibrated models, in addition to the aforementioned linear models, are certain canonical link generalized linear models and nonparametric regressions constructed from them by a particular style of local likelihood fitting. Other models can often be made robust by using a suboptimal fitting method. Thus the class of model-based, but design consistent, analyses is enlarged to include more realistic models for certain types of survey variable such as binary indicators and counts. Particular applications discussed are the estimation of the size of a population subdomain, as arises in tax auditing for example, and the estimation of a bootstrap tail probability.