A Person-Centered Approach to Commitment Research: Theory, Research, and Methodology

There has been a recent increase in the application of person-centered research strategies in the investigation of workplace commitments. To date, research has focused primarily on the identification, within a population, of subgroups presenting different cross-sectional or longitudinal configurations of commitment mindsets (affective, normative, continuance) and/or targets (e.g., organization, occupation, supervisor), but other applications are possible. In an effort to promote a substantive-methodological synergy, we begin by explaining why some aspects of commitment theory are best tested using a person-centered approach. We then summarize the result of existing research and suggest applications to other research questions. Next, we turn our attention to methodological issues, including strategies for identifying the best profile structure, testing for invariance across samples, time, culture, etc., and incorporating other variables in the models to test theory regarding profile development, consequences, and change trajectories. We conclude with a discussion of the practical implications of taking a person-centered approach to the study of commitment as a complement to the more traditional variable-centered approach

Genome-Wide Association Study in Bipolar Patients Stratified by Co-Morbidity

Bipolar disorder is a severe psychiatric disorder with high heritability. Co-morbid conditions are common and might define latent subgroups of patients that are more homogeneous with respect to genetic risk factors.In the Caucasian GAIN bipolar disorder sample of 1000 cases and 1034 controls, we tested the association of single nucleotide polymorphisms with patient subgroups defined by co-morbidity.). All three associations were found under the recessive genetic model. Bipolar disorder with low probability of co-morbid conditions did not show significant associations.Conceptualizing bipolar disorder as a heterogeneous disorder with regard to co-morbid conditions might facilitate the identification of genetic risk alleles. Rare variants might contribute to the susceptibility to bipolar disorder

Probabilistic subgroup identification using Bayesian finite mixture modelling: A case study in Parkinson's disease phenotype identification

This article explores the use of probabilistic classification, namely finite mixture modelling, for identification of complex disease phenotypes, given cross-sectional data. In particular, if focuses on posterior probabilities of subgroup membership, a standard output of finite mixture modelling, and how the quantification of uncertainty in these probabilities can lead to more detailed analyses. Using a Bayesian approach, we describe two practical uses of this uncertainty: (i) as a means of describing a person’s membership to a single or multiple latent subgroups and (ii) as a means of describing identified subgroups by patient-centred covariates not included in model estimation. These proposed uses are demonstrated on a case study in Parkinson’s disease (PD), where latent subgroups are identified using multiple symptoms from the Unified Parkinson’s Disease Rating Scale (UPDRS)