We present an algorithm for learning correlations among link types and node attributes in relational data that represent complex heterogeneous networks. The link correlations are represented in a Bayes net structure. The current state of the art algorithm for learning relational Bayes nets captures only correlations among entity attributes given the existence of links among entities. The models...

We investigated the relationships between leaders' and their followers' psychological capital and organizational identification in a Chinese community. Participants included 423 followers on 34 work teams, each with its respective team leader. Hierarchical linear models (HLM) were used in the analyses to delineate the relationships among participants' demographic background (gender, age, marita...

Multilevel logistic regression models are increasingly being used to analyze clustered data in medical, public health, epidemiological, and educational research. Procedures for estimating the parameters of such models are available in many statistical software packages. There is currently little evidence on the minimum number of clusters necessary to reliably fit multilevel regression models. W...

Bayesian hierarchical shrinkage methods have been widely used for quantitative trait locus mapping. From the computational perspective, the application of the Markov chain Monte Carlo (MCMC) method is not optimal for high-dimensional problems such as the ones arising in epistatic analysis. Maximum a posteriori (MAP) estimation can be a faster alternative, but it usually produces only point esti...

New technologies and multilevel data sets that include geographic identifiers have heightened sociologists' interest in spatial analysis. I review several of the key concepts, measures, and methods that are brought into play in this work, and offer examples of their application in a variety of substantive fields. I argue that the most effective use of the new tools requires greater emphasis on ...

The predictive validity of personality for important life outcomes is well established, but conventional longitudinal analyses cannot rule out the possibility that unmeasured third-variable confounds fully account for the observed relationships. Longitudinal hierarchical linear models (HLM) with time-varying covariates allow each subject to serve as his or her own control, thus eliminating betw...

Housing data are of a nested nature as houses are nested in a village, a town, or a county. This study thus applies HLM (hierarchical linear modelling) in an empirical study by adding neighborhood characteristic variables into the model for consideration. Using the housing data of 31 neighborhoods in the Taipei area as analysis samples and three HLM sub-models, this study discusses the impact o...

The computational genome-wide annotation of gene functions requires the prediction of hierarchically structured functional classes and can be formalized as a multiclass, multilabel, multipath hierarchical classification problem, characterized by very unbalanced classes. We recently proposed two hierarchical protein function prediction methods: the Hierarchical Bayes (hbayes) and True Path Rule ...

Cost performance by NASA programs can generate two stable states within the organization system used for program performance management. These states could be stable individually or show multistability with cusp-like behavior. To test this, we constructed a cusp catastrophe model in a multilevel modeling (HLM) procedure on the cost components of an eleven-month period of one NASA program. HLM w...