Variance component models for longitudinal count data with baseline information: epilepsy data revisited

Random effect models have often been used in longitudinal data analysis since they allow for association among repeated measurements due to unobserved heterogeneity. Various approaches have been proposed to extend mixed models for repeated count data to include dependence on baseline counts. Dependence between baseline counts and individual-specific random effects result in a complex form of the (conditional) likelihood. An approximate solution can be achieved ignoring this dependence, but this approach could result in biased parameter estimates and in wrong inferences. We propose a computationally feasible approach to overcome this problem, leaving the random effect distribution unspecified. In this context, we show how the EM algorithm for nonparametric maximum likelihood (NPML) can be extended to deal with dependence of repeated measures on baseline counts.     

Assessment of Organizational Trust: Italian Adaptation and Factorial Validity of the Organizational Trust Inventory

Trust influences interactions among individuals and organizations but has been an elusive concept to define and measure. The Organizational Trust Inventory (OTI) measures three dimensions of organizational trust, as defined by Cummings and Bromiley (in: Kramer and Tyler (eds) Trust in Organizations, 1996), believing or feeling that others: keep commitments, negotiate honestly, and do not take excessive advantage. This research presents an original adaptation of the OTI for the Italian people. It can now be used to evaluate trust regarding colleagues, managers, suppliers, subordinates, clients, and organizations. Using a sample of 490 employees and structural equation modeling, we consider the validity of the questionnaire and the theoretical model articulation both across different counterpart and countries. Besides, we develop a new reduced-form of the questionnaire (OTI/R), which offers better psychometric properties than the long form.     

Structure and Culture in Social Movement Theory

Sociological Forum -     

A general function of axiomatic index numbers

Summary This paper describes an axiomatic approach to index number theory. A general bilaeral formula which generates a set of indices of prices and quantities is proposed. This formula is written as a geometric mean, weighted with logarithmic means of relative values, and includes all of those indices and related cofactors which satisfy the following axiomatic properties: strong identity, commensurability, linear homogeneity and associativity (or monotonicity). Moreover, two subsets of indices are identified: the first subset includes those which can be expressed either as geometric means or as expenditure ratios; the corresponding bounds are given by the Laspeyres' and Paasche's indices. The second subset also satisfies the desired properties of base and factor reversibility; in this case the bounds are given by Sato Vartia's and Fisher's indices. In addition it is shown that the intersection between the two subsets identifies a new bilateral ideal index which satisfies the axiomatic and reversibility properties (base and factor) and may also be written as an expenditure ratio. All other formulas, which do not belong to the family of the proposed indices, violate some of the axiomatic properties. For multilateral comparisons, a mixed system of direct and indirect indices which satisfies the transitivity condition is proposed.     

A finite mixture model for image segmentation

In this paper, we propose a model for image segmentation based on a finite mixture of Gaussian distributions. For each pixel of the image, prior probabilities of class memberships are specified through a Gibbs distribution, where association between labels of adjacent pixels is modeled by a class-specific term allowing for different interaction strengths across classes. We show how model parameters can be estimated in a maximum likelihood framework using Mean Field theory. Experimental performance on perturbed phantom and on real benchmark images shows that the proposed method performs well in a wide variety of empirical situations.     

Organizational access to research settings: Entering secondary schools

Fieldworkers have long acknowledged the problems of gaining access to research settings but often failed to analyze them systematically. In this article we provide preliminary evidence that, when seeking to enter formal bureaucracies, access problems can be better understood and more easily resolved by referring to the organizational logic of the setting. Adopting an organizational perspective to analyze access issues, we compare public schools and private religious schools and show how access problems and the strategies to overcome them vary according to the variation in the schools’ organizational features. We develop our argument in four steps. First, we examine the organizational literature on formal institutionalized organizations to highlight the structural problems of gaining entry to public schools for research purposes. Second, we adopt an organizational perspective to identify strategies for overcoming problems of access to public schools. Third, we compare the access to private religious schools and to public schools to demonstrate that problems and solution strategies vary according to the schools’ variation in organizational features. Fourth, we demonstrate the usefulness of organizational access by showing that researchers’ ability to gain entry is enhanced by a preliminary understanding of the organizational characteristics of a formal research setting. Throughout our discussion, we provide illustrative examples from the senior author’s research in public schools and private religious schools in a large Midwestern city.     

Hierarchical Bayesian level set inversion

The level set approach has proven widely successful in the study of inverse problems for interfaces, since its systematic development in the 1990s. Recently it has been employed in the context of Bayesian inversion, allowing for the quantification of uncertainty within the reconstruction of interfaces. However, the Bayesian approach is very sensitive to the length and amplitude scales in the prior probabilistic model. This paper demonstrates how the scale-sensitivity can be circumvented by means of a hierarchical approach, using a single scalar parameter. Together with careful consideration of the development of algorithms which encode probability measure equivalences as the hierarchical parameter is varied, this leads to well-defined Gibbs-based MCMC methods found by alternating Metropolis–Hastings updates of the level set function and the hierarchical parameter. These methods demonstrably outperform non-hierarchical Bayesian level set methods.     

Constraints and the Measurement of Freedom of Choice

This paper introduces considerations about constraints in the construction of measures of an agent's freedom. It starts with motivating the exercise from both the philosophical and the informational point of view. Then it presents two rankings of opportunity sets based on information about the extent of options and the constraints that a decision maker faces. The first ranking measures freedom as variety of choice; the second as non-restrictedness in choice.