Changes in the quality and regulation of social work education: Confronting the dilemmas of workforce planning and competing qualifications frameworks

The Modernising Social Services agenda seeks to raise the quality of service outcomes through regulation. Because attaining quality outcomes for service users is related to better training of providers, a parallel modernising agenda seeks to raise the quality of education and training through regulation. With the demise of the CCETSW, responsibility for regulating social work education is under debate. Workforce planning is a critical issue, as are trends towards separate qualifications, genericism, and specialism. Two agencies, the QAA (Quality Assurance Agency, responsible for higher education qualification frameworks) and the QCA (Qualifications and Curriculum Agency, responsible for work-based qualification frameworks) have established their separate qualifications frameworks, but have not worked out the transferability and equivalence of educational and work-based awards across the separate frameworks. Until consonance is established, a seamless ladder of progression from work-based social care awards to educationally based social work awards is not possible, thereby limiting workers' expertise and career opportunities. This paper argues that change must involve reconciling separate frameworks and resolving issues of transferability, specialism, and genericism. Social work education's partnerships between universities and employers provide lessons for bridging the divide between work-based and educational awards. Women, part-time workers, ethnic minorities, and people with a disability will benefit from accessible transparent frameworks.     

Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models

When independence is assumed, forecasts of mortality for subpopulations are almost always divergent in the long term. We propose a method for coherent forecasting of mortality rates for two or more subpopulations, based on functional principal components models of simple and interpretable functions of rates. The product-ratio functional forecasting method models and forecasts the geometric mean of subpopulation rates and the ratio of subpopulation rates to product rates. Coherence is imposed by constraining the forecast ratio function through stationary time series models. The method is applied to sex-specific data for Sweden and state-specific data for Australia. Based on out-of-sample forecasts, the coherent forecasts are at least as accurate in overall terms as comparable independent forecasts, and forecast accuracy is homogenized across subpopulations.     

The Siable-Law Model of Stock Returns

This study investigates the tail shapes of empirical distributions of returns on an extensive group of common stocks. The tails of the return distributions are found to be thinner than those of infinite variance stable distributions. Therefore, although homogeneity is evident in general, economic and statistical inferences drawn from stable-law parameters estimated from samples of stock returns may be misleading. This is in spite of the apparent overall similarity (in shape) between empirical and stable distributions.     

Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm

Two new implementations of the EM algorithm are proposed for maximum likelihood fitting of generalized linear mixed models. Both methods use random (independent and identically distributed) sampling to construct Monte Carlo approximations at the E-step. One approach involves generating random samples from the exact conditional distribution of the random effects (given the data) by rejection sampling, using the marginal distribution as a candidate. The second method uses a multivariate t importance sampling approximation. In many applications the two methods are complementary. Rejection sampling is more efficient when sample sizes are small, whereas importance sampling is better with larger sample sizes. Monte Carlo approximation using random samples allows the Monte Carlo error at each iteration to be assessed by using standard central limit theory combined with Taylor series methods. Specifically, we construct a sandwich variance estimate for the maximizer at each approximate E-step. This suggests a rule for automatically increasing the Monte Carlo sample size after iterations in which the true EM step is swamped by Monte Carlo error. In contrast, techniques for assessing Monte Carlo error have not been developed for use with alternative implementations of Monte Carlo EM algorithms utilizing Markov chain Monte Carlo E-step approximations. Three different data sets, including the infamous salamander data of McCullagh and Nelder, are used to illustrate the techniques and to compare them with the alternatives. The results show that the methods proposed can be considerably more efficient than those based on Markov chain Monte Carlo algorithms. However, the methods proposed may break down when the intractable integrals in the likelihood function are of high dimension.     

THE ANALYSIS OF OUTLYING DATA POINTS USING ROBUST REGRESSION: A MULTIVARIATE PROBLEM-BANK IDENTIFICATION MODEL*

Because the eight largest bank failures in United States history have occurred since 1973 [24], the development of early-warning problem-bank identification models is an important undertaking. It has been shown previously [3] [5] that M-estimator robust regression provides such a model. The present paper develops a similar model for the multivariate case using both a robustified Mahalanobis distance analysis [21] and principal components analysis [10]. In addition to providing a successful presumptive problem-bank identification model, combining the use of the M-estimator robust regression procedure and the robust Mahalanobis distance procedure with principal components analysis is also demonstrated to be a general method of outlier detection. The results from using these procedures are compared to some previously suggested procedures, and general conclusions are drawn.     

Responses to Scarcity

Studies of pre-industrial societies suggest that in response to protracted scarcity, individuals experience a decline in social relations, return to traditional practices and values, and become more subordinate to those in authority. In this paper, we examine the extent to which these same responses are found in an industrialized area. Evidence from survey data suggests that scarcity and economic decline do decrease social relations and cause some individuals (those with less formal education) to use more traditional means to solve problems, but they do not cause people to become more passive in respect to community leaders. Factors that may account for the different responses to scarcity in industrial and pre-industrial societies are discussed.     

Transforming gompertz's function for fertility analysis: The development of a standard for the relational gompertz function

Summary The relational Gompertz function improves upon the Gompertz for fertility analysis by achieving a better fit in the tails of the distribution. This is obtained by a transformation of the age scale corresponding to an empirical standard. This standard is developed from Coale and Trussell's model and is appropriate for use with populations of high fertility. The model is tested on two sets of data and is shown to produce good estimates of completed fertility even for data truncated at quite early ages. Good results are also obtained for declining fertility.