the  main  assumptions  in  liner  mixed  model  are normality  and  independency  of  random  effect component.   unfortunately,   these   two  assumptions   might   be  unrealistic   in  some   situations. therefore, in this paper, we will discuss about the analysis of bayesian analysis of non-normal and non-independent mixed model using skew-normal/independent distributions, and finally, thi...

background: the study was developed in order to find a subset of potential factors, which affect birth distance pattern, regarding consideration on correlation of events of birth in a family and correlation within clusters/centers which other studies omit these correlations. methods: referring to documents that were registered for family in the health care centers on socio-economical zone, we c...

This article introduces some applications of multilevel modeling for research on art and creativity. Researchers often collect nested, hierarchical data— such as multiple assessments per person—but they typically ignore the nested data structure by averaging across a level of data. Multilevel modeling, also known as hierarchical linear modeling and random coefficient modeling, enables researche...

In this paper we give worked examples of adding national data to international datasets in order to add value to the secondary analysis of this data. We also show how more sophisticated techniques, including multilevel modelling and structural equation modelling, can be applied to such enhanced data to go beyond the potentially misleading simple correlational analysis. The paper is illustrated ...

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...

Multilevel modeling is often treated as if it concerns only regression analysis and growth modeling. Multilevel modeling, however, is relevant for nested data not only with regression and growth analysis but with all types of statistical analyses. This chapter has two aims. First, it shows that already in the traditional multilevel analysis areas of regression and growth there are several new m...

this research develops a two-level model based on hypotheses, which concern relationships among role ambiguity, role conflict, job involvement and salesperson performance at individual level and collective sale self efficacy, customer orientation and competitive climate at sale unit level in iranian food industry. data was drawn from 482 sales people in 30 companies , using a 51-item self-repor...

Multilevel (hierarchical) modeling is a generalization of linear and generalized linear modeling in which regression coefficients are themselves given a model, whose parameters are also estimated from data. We illustrate the strengths and limitations of multilevel modeling through an example of the prediction of home radon levels in U.S. counties. The multilevel model is highly effective for pr...