Two Stochastic Multidimensional Choice Models for Marketing Research

Two recently developed probabilistic multidimensional models for analyzing pairwise choice data are introduced, discussed in terms of their differential properties, and extended in several ways. The first one, the wandering vector model, was originally suggested by Carroll [12] and extended by De Soete and Carroll [30]. The second model, called the wandering ideal point model, is a more recently proposed [32] unfolding analog of the wandering vector model. A general maximum likelihood estimation method for fitting the various models described is mentioned, as well as a statistical test for assessing the goodness of fit. Finally, an application of the models is provided concerning consumer choice for some 14 brands of over-the-counter analgesics to illustrate how such models can be gainfully utilized for marketing decision making concerning product positioning.     

‘I Need Help on Mondays,It’s Not My Day. The Other Days,I’m OK’.—Perspectives of Disabled Children on Inclusive Education

This article examines the experience of inclusive education from the perspective of disabled children. We worked with the observations of, and interviews with, 15 children, aged 5–17 who go to a mainstream school. The study is set in the context of a 3‐year research project exploring the practice of inclusive education in Flanders. Here, we report on the key findings from the children’s accounts, focusing on what they had to say about themselves, what they think about school, friends, support and their future prospects.     

Sensitivity analysis for incomplete contingency tables: the Slovenian plebiscite case

Classical inferential procedures induce conclusions from a set of data to a population of interest, accounting for the imprecision resulting from the stochastic component of the model. Less attention is devoted to the uncertainty arising from (unplanned) incompleteness in the data. Through the choice of an identifiable model for non-ignorable non-response, one narrows the possible data-generating mechanisms to the point where inference only suffers from imprecision. Some proposals have been made for assessing the sensitivity to these modelling assumptions; many are based on fitting several plausible but competing models. For example, we could assume that the missing data are missing at random in one model, and then fit an additional model where non-random missingness is assumed. On the basis of data from a Slovenian plebiscite, conducted in 1991, to prepare for independence, it is shown that such an ad hoc procedure may be misleading. We propose an approach which identifies and incorporates both sources of uncertainty in inference: imprecision due to finite sampling and ignorance due to incompleteness. A simple sensitivity analysis considers a finite set of plausible models. We take this idea one step further by considering more degrees of freedom than the data support. This produces sets of estimates (regions of ignorance) and sets of confidence regions (combined into regions of uncertainty).     

Joint modelling of longitudinal response and time-to-event data using conditional distributions: a Bayesian perspective

Over the last 20 or more years a lot of clinical applications and methodological development in the area of joint models of longitudinal and time-to-event outcomes have come up. In these studies, patients are followed until an event, such as death, occurs. In most of the work, using subject-specific random-effects as frailty, the dependency of these two processes has been established. In this article, we propose a new joint model that consists of a linear mixed-effects model for longitudinal data and an accelerated failure time model for the time-to-event data. These two sub-models are linked via a latent random process. This model will capture the dependency of the time-to-event on the longitudinal measurements more directly. Using standard priors, a Bayesian method has been developed for estimation. All computations are implemented using OpenBUGS. Our proposed method is evaluated by a simulation study, which compares the conditional model with a joint model with local independence by way of calibration. Data on Duchenne muscular dystrophy (DMD) syndrome and a set of data in AIDS patients have been analysed.     

Validation of surrogate end points in multiple randomized clinical trials with failure time end points

Before a surrogate end point can replace a final (true) end point in the evaluation of an experimental treatment, it must be formally 'validated'. The validation will typically require large numbers of observations. It is therefore useful to consider situations in which data are available from several randomized experiments. For two normally distributed end points Buyse and co-workers suggested a new definition of validity in terms of the quality of both trial level and individual level associations between the surrogate and true end points. This paper extends this approach to the important case of two failure time end points, using bivariate survival modelling. The method is illustrated by using two actual sets of data from cancer clinical trials.     

Simple Fitting Algorithms for Incomplete Categorical Data

A popular approach to estimation based on incomplete data is the EM algorithm. For categorical data, this paper presents a simple expression of the observed data log-likelihood and its derivatives in terms of the complete data for a broad class of models and missing data patterns. We show that using the observed data likelihood directly is easy and has some advantages. One can gain considerable computational speed over the EM algorithm and a straightforward variance estimator is obtained for the parameter estimates. The general formulation treats a wide range of missing data problems in a uniform way. Two examples are worked out in full.     

High dimensional multivariate mixed models for binary questionnaire data

Summary.  Questionnaires that are used to measure the effect of an intervention often consist of different sets of items, each set possibly measuring another concept. Mixed models with set-specific random effects are a flexible tool to model the different sets of items jointly. However, computational problems typically arise as the number of sets increases. This is especially true when the random-effects distribution cannot be integrated out analytically, as with mixed models for binary data. A pairwise modelling strategy, in which all possible bivariate mixed models are fitted and where inference follows from pseudolikelihood theory, has been proposed as a solution. This approach has been applied to assess the effect of physical activity on psychocognitive functioning, the latter measured by a battery of questionnaires.     

Les professions sociales en France: Du modèle de la qualification au modèle de la compétence?

In the social professions, is the qualifications model going to be driven out by a second model, that of the management of skills, as seen in many other sectors of activity? The social professions used to be accepted in their employment sectors in line with the statutory logic of qualifications. However, the recent introduction of a skills management logic privileging individual aptitudes, advantages gained from a variety of professional experiences, and the capacity to conform to the objectives of an employing organization could lead to less regard for original qualifications, and to a decline in any correspondence between a diploma or degree and the individual's position within the repertory of salaried jobs and classifications. One can in fact note an increased disjunction between titles, functions, and status, especially in the newer activity areas (insertion, city/town, policies, mediation, etc.). In contrast, one can still observe a very firmly maintained linkage between the qualification and the job in the traditional sectors (social services, work with the disabled, specialized education, etc.). However, the widening of social interventions beyond the frames of classical action areas, and their extension to areas such as accommodation and home help, along with the progressive modification of organizational models, leads one to wonder whether the social professions will retain their special character, or whether, to the contrary, they will experience a process of differentiation into sub-sectors with reference to alternative operational logics. Will the future of the social professions oscillate between assignation to employment sectors where the qualifications logic reigns (and which could become residual), and entry into new intervention areas where the 'battle of the skills' will be fought? Whatever the outcome, the debate about the professionalism of social workers should surely rise above any such presumed antagonisms.     

Measuring Young Children's Perceptions of Support, Control, and Maintenance in Their Own Social Networks

Thirty‐eight children (aged 3;7–7;6) and one of their parents took part in a study concerning children's perceptions of their social networks. The study made use of a newly developed instrument—the Support, Control and Maintenance Pictures Interview (SCAMPI). The SCAMPI offers an individualized testing environment, employing computer presentation of questions based on photographs of significant persons familiar to the child. SCAMPI is designed to allow data analysis to be carried out with the aid of built‐in statistical procedures based on permutation and bootstrap techniques that are optimally adapted to the requirements of individualized testing. The study examines the differentiations young children make between persons in their social networks and the functions they fulfil, the level of agreement between children and their parents and the stability of the children's responses.