In present study, it was examined that how parent collaboration with school and student, affect students’ math achievement, and how this effect varies across schools. . (NELS) data set includes 24.599 8 grade students from 1042 high schools throughout the U.S. in 1988. In this data set some of the topics covered are school, work, parent’s educational background. By using two-level HLM), an atte...

Growth curve analyses for couples pose special analytic problems when the data are available from both partners and the partners can be distinguished from each other (such as husband and wife or male partner and female partner). In this case, the unit of analysis is the couple rather than the individual (see Raudenbush, Brennan, & Barnett, 1995). (By extension, the family could also be the unit...

Most data involving organizations are hierarchical in nature and often contain variables measured at multiple levels of analysis. Hierarchical linear modeling (HLM) is a relatively new and innovative statistical method that organizational scientists have used to alleviate some common problems associated with multilevel data, thus advancing our understanding of organizations. This article presen...

This paper seeks to advance research on interpersonal exchange relationships between supervisors, subordinates, and coworkers at work by integrating social exchange, workplace friendship, and climate research to develop a multi-level model. We tested the model using hierarchical linear modeling (HLM) with data obtained from a sample of 215 manager-employee dyads working in 36 teams. At the indi...

Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests andplotting the conditional relations is often a tedious and error-prone ...

This paper describes generic approaches for georeferencing multilingual textual annotations and sets of tags from metadata associated to textual or multimedia content with high precision. We present four approaches based on: 1) Geographical Knowledge, 2) Language Modelling (LM), 3) Language Modelling with Re-Ranking predictions, 4) Fusion of Geographical Knowledge predictions with the other app...

The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Different strategies have been proposed to solve this problem, however, rigorous analysis justifying their performance is missing. This paper suggests the Theoretical Spectral Curvature Clustering (TSCC) algorithm for solving the HLM problem and provides careful analysis to justify it. ...

Beauvericin is a secondary metabolite natural product from microorganisms and has been shown to have a new potential antifungal activity. In this study, the metabolism and inhibition of beauvericin in human liver microsomes (HLM) and rat liver microsomes (RLM) were investigated. The apparent K(m) and V(max) of beauvericin in HLM were determined by substrate depletion approach and its inhibitory...

Many data sets analyzed in human and social sciences have a multilevel or hierarchical structure. By hierarchy we mean that units of a certain level (also referred micro units) are grouped into, or nested within, higher level (or macro) units. In these cases, the units within a cluster tend to be more different than units from other clusters, i.e., they are correlated. Thus, unlike in the class...