A praxis and Research Agenda for Multicultural Human Services Organizations

Human services organizations in the United States are currently faced with the problems of a diminishing and shifting resource base coupled with increasing demands for services. In addition, national demographic changes with increasing social diversity, and a rise in racial, ethnic, gender and other group-based tensions have posed special challenges for human service organizations. By tracing the history of social work approaches to working with ethnic minorities, this paper argues that responses to environmental flux have in fact been inadequate in addressing issues of social inequalities and injustices. We articulate a vision of socially just and diverse human services organizations that are both empowering and empowered. We call these Multicultural Human Services Organizations (MHSO). We spell out praxis and research agendas that continue the social justice-social diversity mission that is central to our vision.     

On the Critical Value Behaviour of Multiple Decision Procedures

In this paper we investigate the asymptotic critical value behaviour of certain multiple decision procedures as e.g. simultaneous confidence intervals and simultaneous as well as stepwise multiple test procedures. Supposing that n hypotheses or parameters of interest are under consideration we investigate the critical value behaviour when n increases. More specifically, we answer e.g. the question by which amount the lengths of confidence intervals increase when an additional parameter is added to the statistical analysis. Furthermore, critical values of different multiple decision procedures as for instance step-down and step-up procedures will be compared. Some general theoretic results are derived and applied for various distributions.     

Sense of coherence and social support – Resources for subjective well-being and health of the aged in Finland

In this study we explored if the psychological and social resources of aged people (over 75 years) in Finland predict their subjective well-being and experienced state of health (n = 348). Data were taken from a larger Finnish survey on living conditions. Based on previous research on younger people we formed a model where morbidity, experienced quality of social support and sense of coherence together with economic resources are the predictors of both experienced state of health and subjective well-being. LISREL (8) path analysis was used to test the model. The model providing the most parsimonious explanation of the data suggested that a strong sense of coherence and high experienced quality of social relationships are strongly related to subjective well-being. Experienced state of health was associated with morbidity and subjective well-being, but there was no significant relationship between subjective well-being and morbidity.     

Application of PBIB designs in CDC Method IV

In this paper we propose the use of some partially balanced incomplete block designs for blocking in complete diallel cross Method IV (Griffing, 1956) to deal with the situation when it is not desirable for all crosses to be accommodated in the block of a traditional randomized block design. A method is also proposed to analyse the MatingEnvironment designs for estimating the general combining ability effect of lines.     

Choice Functions: Rationality re-Examined

On analyzing the problem that arises whenever the set of maximal elements is large, and a selection is then required (see Peris & Subiza 1998), we realize that logical ways of selecting among maximals violate the classical notion and axioms of rationality. We arrive at the same conclusion if we analyze solutions to the problem of choosing from a tournament (where maximal elements do not necessarily exist). So, in our opinion the notion of rationality must be discussed, not only in the traditional sense of external conditions (Sen 1993), but in terms of the internal information provided by the binary relation.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

Consistent Estimation of the Structural Distribution Function

Motivated by problems in linguistics we consider a multinomial random vector for which the number of cells N is not much smaller than the sum of the cell frequencies, i.e. the sample size n . The distribution function of the uniform distribution on the set of all cell probabilities multiplied by N is called the structural distribution function of the cell probabilities. Conditions are given that guarantee that the structural distribution function can be estimated consistently as n increases indefinitely although n / N does not. The natural estimator is inconsistent and we prove consistency of essentially two alternative estimators.     

Bayes linear analysis for graphical models: The geometric approach to local computation and interpretive graphics

This paper concerns the geometric treatment of graphical models using Bayes linear methods. We introduce Bayes linear separation as a second order generalised conditional independence relation, and Bayes linear graphical models are constructed using this property. A system of interpretive and diagnostic shadings are given, which summarise the analysis over the associated moral graph. Principles of local computation are outlined for the graphical models, and an algorithm for implementing such computation over the junction tree is described. The approach is illustrated with two examples. The first concerns sales forecasting using a multivariate dynamic linear model. The second concerns inference for the error variance matrices of the model for sales, and illustrates the generality of our geometric approach by treating the matrices directly as random objects. The examples are implemented using a freely available set of object-oriented programming tools for Bayes linear local computation and graphical diagnostic display.     

On optimal testing for the equality of equicorrelation: An example of loss in power

Sen Gupta (1988) considered a locally most powerful (LMP) test for testing nonzero values of the equicorrelation coefficient of a standard symmetric multivariate normal distribution. This paper constructs analogous tests for the symmetric multivariate normal distribution. It shows that the new test is uniformly most powerful invariant even in the presence of a nuisance parameter, σ². Further applications of LMP invariant tests to several equicorrelated populations have been considered and an extension to panel data modeling has been suggested.     

Importance of interpolation when constructing double-bootstrap confidence intervals

We show that, in the context of double-bootstrap confidence intervals, linear interpolation at the second level of the double bootstrap can reduce the simulation error component of coverage error by an order of magnitude. Intervals that are indistinguishable in terms of coverage error with theoretical, infinite simulation, double-bootstrap confidence intervals may be obtained at substantially less computational expense than by using the standard Monte Carlo approximation method. The intervals retain the simplicity of uniform bootstrap sampling and require no special analysis or computational techniques. Interpolation at the first level of the double bootstrap is shown to have a relatively minor effect on the simulation error.