Effective educators are culturally competent communicators

Effective formal education or schooling is not simply a matter of teaching and learning curriculum content. It is also about values, assumptions, feelings, perceptions and relationships. No education can take place without interpersonal communication. Effective teaching can thus be qualified in terms of relating effectively in the classroom. Effective education thus also presupposes effective communication skills. Communication as the means and indeed the medium of education is therefore crucial to school success in culturally diverse education. Teachers should therefore be sensitive to the potentially problematic outcomes of intercultural communication in the culturally diverse class. Communication may be a useful source of intercultural knowledge and mutual enrichment between culturally diverse students if managed proactively by the teacher. Otherwise, it could be a source of frustration, misapprehensions, intercultural conflict and ultimately school failure. Effective and successful communication is difficult to realise even in the most favourable circumstances. Intercultural factors, therefore, create the potential for numerous communication problems and intercultural conflict. Communication, both verbal as well as non-verbal, is critically important in cross-cultural competence. As language and culture are inextricably bound, cross-cultural communication is complex and potentially problematic. In this paper, it is argued that effective educators are effective communicators and thus culturally competent in cross-cultural encounters.     

On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses

Statistical experiments, more commonly referred to as Monte Carlo or simulation studies, are used to study the behavior of statistical methods and measures under controlled situations. Whereas recent computing and methodological advances have permitted increased efficiency in the simulation process, known as variance reduction, such experiments remain limited by their finite nature and hence are subject to uncertainty; when a simulation is run more than once, different results are obtained. However, virtually no emphasis has been placed on reporting the uncertainty, referred to here as Monte Carlo error, associated with simulation results in the published literature, or on justifying the number of replications used. These deserve broader consideration. Here we present a series of simple and practical methods for estimating Monte Carlo error as well as determining the number of replications required to achieve a desired level of accuracy. The issues and methods are demonstrated with two simple examples, one evaluating operating characteristics of the maximum likelihood estimator for the parameters in logistic regression and the other in the context of using the bootstrap to obtain 95% confidence intervals. The results suggest that in many settings, Monte Carlo error may be more substantial than traditionally thought.     

Characterization of Unacceptable Solutions in LP Discriminant Analysis*

Many linear programming models have been proposed for performing discriminant analysis. Partial characterizations for unacceptable solutions have been presented and new models proposed to circumvent these problems. In this paper those conditions leading to unacceptable solutions for all two-group models are characterized.     

Unclaimed Prize Information Biases Perceptions of Winning in Scratch Card Gambling

Unclaimed prize information (i.e., the number of prizes still available to be won) is information commonly provided to scratch card gamblers. However, unless the number of tickets remaining to be purchased is also provided, this information is uninformative. Despite its lack of utility in assisting gamblers in choosing the most favourable type of scratch card to play, we hypothesized that unclaimed prize information would bias participants’ judgments within a scratch card gambling context. In Experiment 1 (N?=?201), we showed that participants are influenced by this information such that they felt more likely to win, were more excited to play, and preferred to hypothetically purchase more of the scratch card with the greatest number of unclaimed prizes. In Experiment 2 (N?=?201), we attempted to ameliorate this bias by providing participants with the number of tickets remaining to be purchased and equating the payback percentages of all three games. The bias, although attenuated, still persisted in these conditions. Finally, in Experiment 3 (N?=?200), we manipulated the hypothetical scratch cards such that games with the highest number of unclaimed prizes were the least favourable, and vice versa. As in Experiment 2, participants still favoured cards with greater numbers of unclaimed prizes. Possible mechanisms underlying this bias are discussed. In conclusion, across three experiments, we demonstrate that salient unclaimed prize information is capable of exerting a strong effect over judgments related to scratch card games.     

Stochastic Forecasting of Labor Supply and Population: An Integrated Model

This paper presents a stochastic model to forecast the German population and labor supply until 2060. Within a cohort-component approach, our population forecast applies principal components analysis to birth, mortality, emigration, and immigration rates, which allows for the reduction of dimensionality and accounts for correlation of the rates. Labor force participation rates are estimated by means of an econometric time series approach. All time series are forecast by stochastic simulation using the bootstrap method. As our model also distinguishes between German and foreign nationals, different developments in fertility, migration, and labor participation could be predicted. The results show that even rising birth rates and high levels of immigration cannot break the basic demographic trend in the long run. An important finding from an endogenous modeling of emigration rates is that high net migration in the long run will be difficult to achieve. Our stochastic perspective suggests therefore a high probability of substantially decreasing the labor supply in Germany.     

SPECIFICATION AND ESTIMATION OF LOGICALLY CONSISTENT LINEAR MODELS

The implications of constrained dependent and independent variables for model parameters are examined. In the context of linear model systems, it is shown that polyhedral constraints on the dependent variables will hold over the domain of the independent variables when a set of polyhedral constraints is satisfied by the model parameters. This result may be used in parameter estimation, in which case all predicted values of the dependent variables are consistent with constraints on the actual values. Also, the implicit constraints that define the set of parameters for many commonly used linear stochastic models with an error term yield values of the dependent variables consistent with the explicit constraints. Models possessing these properties are termed “logically consistent”.     

Social dynamics of the multicultural classroom

Instruction and learning are socially determined activities, where social forces such as classroom atmosphere, social feelings, cultural sentiments, prejudice and stereotyping, interpersonal relations and expectations, as well as the reflection of social reality in subject matter all have a significant influence on the effectiveness of teaching and learning. The effective "multicultural" teacher has to be concerned about each individual student, and also be sensitive to the group and cultural affiliations of each of his or her students. Intercultural relations in the classroom may be a source of knowledge and mutual enrichment between culturally diverse learners if managed proactively by teachers. Frustration, misapprehensions and intercultural conflict are a more likely outcome if teachers do not deal with diversity in a sensitive manner.