Card sorts: Constructivist assessment tools

In a changing employment climate and with the growth of demand for careers guidance at all stages of life, careers guidance practice has moved from its positivist world view, with the counsellor as expert and client as passive responder, to more holistic 'constructivist' approaches. In essence, these approaches view the career as a holistic concept in which work and personal life are inextricably intertwined, and individuals are experts in their own lives, actively constructing their careers. The first to fully explore the constructivist approach, this book: provides a theoretical background to constructivism; outlines a range of constructivist approaches to career counselling; and gives examples of the practical application of constructivism. Essential for anyone involved in career guidance wishing to learn more about this vital new approach, this book combines theory with practicable guidance, and represents a new direction for career counselling.

A Comparison of Bayesian and Sampling Theory Inferences in a Probit Model

HE PROBIT MODEL IS A POPULAR DEVICE for explaining binary choice decisions in econometrics. It has been used to describe choices such as labor force participation, travel mode, home ownership, and type of education. These and many more examples can be found in papers by Amemiya (1981) and Maddala (1983). Given the contribution of economics towards explaining such choices, and given the nature of data that are collected, prior information on the relationship between a choice probability and several explanatory variables frequently exists. Bayesian inference is a convenient vehicle for including such prior information. Given the increasing popularity of Bayesian inference it is useful to ask whether inferences from a probit model are sensitive to a choice between Bayesian and sampling theory techniques. Of interest is the sensitivity of inference on coefficients, probabilities, and elasticities. We consider these issues in a model designed to explain choice between fixed and variable interest rate mortgages. Two Bayesian priors are employed: a uniform prior on the coefficients, designed to be noninformative for the coefficients, and an inequality restricted prior on the signs of the coefficients. We often know, a priori, whether increasing the value of a particular explanatory variable will have a positive or negative effect on a choice probability. This knowledge can be captured by using a prior probability density function (pdf) that is truncated to be positive or negative. Thus, three sets of results are compared:those from maximum likelihood (ML) estimation, those from Bayesian estimation with an unrestricted uniform prior on the coefficients, and those from Bayesian estimation with a uniform prior truncated to accommodate inequality restrictions on the coefficients.