MEANINGFUL REGRESSION AND ASSOCIATION MODELS FOR CLUSTERED ORDINAL DATA

Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, the association structure is often of equal interest—for example, temporal association in longitudinal studies and association between responses to similar questions in a survey. We discuss the use, appropriateness, and interpretability of various latent variable and Markov models for the association structure and propose a new structure that exploits the ordinality of the response. The models are illustrated with a study concerning opinions regarding government spending and an analysis of stability and change in teenage marijuana use over time, where we reveal different behavioral patterns for boys and girls through a comprehensive investigation of individual response profiles.     

Stability and Change of the Quality of Working Life in Restructuring Municipalities

Social Indicators Research - Local government in Finland has undergone considerable restructuring since 2005 as a number of new larger municipalities (mergers) and partnership areas between social...     

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal‐prevalence‐concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch‐specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within‐batch and between‐batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.     

Learning discrete decomposable graphical models via constraint optimization

Statistical model learning problems are traditionally solved using either heuristic greedy optimization or stochastic simulation, such as Markov chain Monte Carlo or simulated annealing. Recently, there has been an increasing interest in the use of combinatorial search methods, including those based on computational logic. Some of these methods are particularly attractive since they can also be successful in proving the global optimality of solutions, in contrast to stochastic algorithms that only guarantee optimality at the limit. Here we improve and generalize a recently introduced constraint-based method for learning undirected graphical models. The new method combines perfect elimination orderings with various strategies for solution pruning and offers a dramatic improvement both in terms of time and memory complexity. We also show that the method is capable of efficiently handling a more general class of models, called stratified/labeled graphical models, which have an astronomically larger model space.     

Managing testing activities in telecommunications: A case study

This paper examines ways of managing testing more efficiently and effectively. The importance and the workload of testing have been growing constantly in the telecommunication industry, as a result of shorter product lifecycles and decreased time-to-market. At the same time, the complexity of products has increased with a decrease in physical dimensions. Managers face enormous challenges in developing knowledge and effective testing processes. This case study proposes the application of two models, a testing optimization model and an extended V2M2 (Verification Validation Maturity Model) model, for managing the value of testing. In the empirical part of the study, the present state of testing management is examined.     

Promotions and Earnings – Gender or Merit? Evidence from Longitudinal Personnel Data

This study examines the determinants of promotions, performance evaluations and earnings using unique longitudinal data from the personnel records of a large university. The study focuses on the role of gender in remuneration using, first, information on the complexity ratings of job tasks to define promotions on job ladders and, second, information on objective individual productivity. The study finds that individual research productivity was an important determinant of promotions and earnings. The results indicate that gender has no effect on the probability of being promoted, conditional on productivity, nor does it play a role in the performance evaluation of employees. Furthermore, the results suggest that contemporaneous productivity measures provide a usable proxy for the past productivity of a worker.     

Organizational and supervisory justice effects on experienced threat during change: The moderating role of leader in-group representativeness

We explore the complex interplay between organizational justice and supervisory justice when predicting group members' threat perceptions in a context of organizational change. Based on the assumptions of relational models of procedural justice and prior research done in the multifoci justice framework, we hypothesize that the extent to which a supervisor is seen to embody and represent key in-group attributes will moderate the interaction between the supervisor's own interactional justice and the overall organizational procedural justice. Specifically, organizational justice is expected to decrease employees' feelings of threat particularly when the supervisor treats group members fairly and is perceived to be in-group representative rather than non-representative. We found support for this hypothesis across two studies, a cross-sectional survey and a scenario experiment. The findings confirm the particularly powerful role that an in-group representative leader's interactional fairness has in managing group members' responses to fundamental organizational processes. The implications for further research and practice are discussed.     

Transition to Work: Effects of Preparedness and Goal Construction on Employment and Depressive Symptoms

This study examines the mediating role of employment preparedness in improving employment, mental health, and construction of work‐life goals among young vocational school graduates who participated in the School‐to‐Work effectiveness trial. The trial included a 1‐week intervention program that focused on enhancing employment preparedness. In this trial, 416 graduates of secondary vocational institutes were randomized into a control and experimental group. All the study participants were assessed at baseline, immediately after the intervention, and 10 months later. The results showed that the School‐to‐Work intervention increased employment preparedness, which in turn increased employment at the 10‐month follow‐up. Furthermore, employment predicted work‐life‐related personal goals and lower financial strain, which in turn was associated with lower depressive symptoms. The implications for theory and practice are discussed.     

Labelled Graphical Models

A class of log‐linear models, referred to as labelled graphical models (LGMs), is introduced for multinomial distributions. These models generalize graphical models (GMs) by employing partial conditional independence restrictions which are valid only in subsets of an outcome space. Theoretical results concerning model identifiability, decomposability and estimation are derived. A decision theoretical framework and a search algorithm for the identification of plausible models are described. Real data sets are used to illustrate that LGMs may provide a simpler interpretation of a dependence structure than GMs.